Notebook_QBSolv_community_detection.ipynb

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    "# Tutorial for Community Detection using Hybrid Quantum Annealing on Amazon Braket\n",
    "\n",
    "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
    "SPDX-License-Identifier: MIT-0"
   ]
  },
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   "source": [
    "This tutorial is related to the blog post on ['Community Detection in Complex Networks using Hybrid Quantum Annealing on Amazon Braket'](https://aws.amazon.com/blogs/quantum-computing/community-detection-in-complex-networks-using-hybrid-quantum-annealing-on-amazon-braket-part-i/).\n",
    "\n",
    "[Community structure detection](https://en.wikipedia.org/wiki/Community_structure) is an important problem in network analysis. Applications can be found for example in biology for atomistic representation of complex proteins, in social media for the network analysis of human communication, and in business operations for customer segmentation and fraud detection. Overall, the goal of community detection is to detect the appearance of densely connected groups of nodes in a graph, with only sparser connections between groups.\n",
    "\n",
    "In this tutorial, we provide a step-by-step guide on how to formulate community detection as a Quadratic Unconstrained Binary Optimization ([QUBO](https://en.wikipedia.org/wiki/Qubo)) problem. We then demonstrate how to use the open source [QBSolv library](https://github.com/dwavesystems/qbsolv) which offers a D-Wave hybrid solver to solve community detection problems on Amazon Braket."
   ]
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    "## Table of contents\n",
    "* [Modularity-based Community Detection](#Modularity)\n",
    "* [Community Detection as a QUBO Problem](#QUBO_matrix)\n",
    "    * [Two communities (k=2)](#two_communities)\n",
    "    * [Multiple communities](#multi_communities)\n",
    "        * [One-Hot Encoding](#one_hot_encoding)\n",
    "        * [Generalized QUBO formulation](#qubo_formulation)\n",
    "* [Datasets](#datasets)\n",
    "* [List of Key Functions for Community Detection](#list_functions)\n",
    "* [Set Up Environment](#Set_Up_Environment)\n",
    "    * [Helper Functions for Community Detection](#functions)\n",
    "* [Download Graph Data](#download_graph)\n",
    "* [Prepare Graph for Community Detection](#create_graph)\n",
    "    * [Built-In Graph](#Built-in_Graph)\n",
    "    * [Graph from a Local Data File](#local_graph)\n",
    "    * [Random Partition Graph](#random_graph)\n",
    "* [Apply QBSolv for Community Detection](#QBSolv_run)\n",
    "    * [Set Community Parameters for a Graph](#param_graph)\n",
    "    * [Set Hyperparameters for QBSolv](#hyperparam_QBSolv)\n",
    "    * [Execute QBSolv Classical Solver](#run_qbsolv_classical)\n",
    "    * [Execute QBSolv Hybrid Solver](#run_qbsolv_hybrid)\n",
    "* [Detect Communities for Synthetic and Real-World Graphs](#Supplementary)\n",
    "    * [Study a Set of Synthetic Graphs](#random_graph_study)\n",
    "    * [Study a Set of Real-World Graphs](#real_graph_study)"
   ]
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    "## Modularity-based Community Detection <a name=\"Modularity\"></a>\n",
    "\n",
    "The general notion of community structure in complex networks was first proposed and analyzed by Girvan and Newman [[1](https://www.pnas.org/content/99/12/7821)]. The basic idea is to divide a network (or graph) into sets of nodes belonging to different communities (also called clusters), where nodes within any of these communities are highly connected (high intra-connectivity), while nodes in different communities are less connected (low inter-connectivity).\n",
    "\n",
    "To quantify the quality of a network division into communities, Newman and Girvan introduced a metric called modularity $M$. The modularity compares the connectivity of edges within communities with the connectivity of a network (the *null model*) where edges would be randomly placed, but under the constraint that expected degree of each node matches the degree of the node in the original graph [[2](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.69.026113)].\n",
    "\n",
    "Formally, we consider a graph $G$ with corresponding adjacency matrix $A_{ij}$ describing the weight between nodes $i$ and $j$. For the null model, the expected number of edges between nodes $i$ and $j$ is (approximately) given by $g_{i}g_{j}/2m$ [[3](https://en.wikipedia.org/wiki/Modularity_(networks))], where $g_{i}=\\sum_{j}A_{ij}$ is the degree of node $i$ and $m=1/2 \\sum_{i} g_{i}$ is the total number of weights in the graph. Taking the random graph as null model, one can then define the modularity $M$ as $A_{ij} - g_{i}g_{j}/2m$, summed over all pairs of vertices $i,j$ that fall within in the same group.\n",
    "\n",
    "Introducing a conventional pre-factor for normalization, we then arrive at the modularity metric $M$ defined as\n",
    "\n",
    "\\begin{equation*}\n",
    "M = \\frac{1}{2m} \\sum_{ij} \\left( A_{ij} - \\frac{g_ig_j}{2m}\\right)\\delta(c_i,c_j),\n",
    "\\tag{1}\n",
    "\\label{eq:modularity-general}\n",
    "\\end{equation*}\n",
    "\n",
    "where the Kronecker delta $\\delta(c_i, c_j)$ is $1$ if node $i$ and node $j$ are in the same community, and $0$ otherwise.\n",
    "\n",
    "The search for the optimal (largest) modularity value is known to be an NP-hard problem [[4](https://arxiv.org/abs/1608.00163)], with the space of possible partitions growing exponentially with the system size. The goal is then to maximize the modularity $M$ by assigning optimal community assignments $c_i$ to every node in the graph.\n",
    "\n",
    "**The code implementation for the modularity matrix** can be found in `./src/qubo_community/modularity_mx`."
   ]
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    "## Community Detection as a QUBO Problem <a name=\"QUBO_matrix\"></a>\n",
    "\n",
    "Many heuristic search algorithms have been developed for the community detection problem [[4](https://arxiv.org/abs/1608.00163)]. In this tutorial, we focus on formulating the community detection problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem as outlined in Ref. [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538)], and demonstrate how to use D-Wave's `QBSolv` solver on Amazon Braket to detect two or more communities in a given network.\n",
    "\n",
    "### Two communities ($k=2$) <a name=\"two_communities\"></a>\n",
    "\n",
    "Let us first consider the case where we look for a graph partitioning into $k=2$ communities. In this case, we can use binary spin variables $s_{i}\\in\\{-1, 1\\}$ (or bit variables $x_{i}\\in\\{0, 1\\}$) to encode which community node $i$ belongs to. \n",
    "Using the fact that the quantity $(1+s_{i}s_{j})/2$ equals $1$ if nodes $i$ and $j$ belong to the same community, and $0$ otherwise, the modularity matrix can be expressed in compact form as [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538)]:\n",
    "\n",
    "\\begin{equation*}\n",
    "M = \\frac{1}{4m}\\vec{s}^T B \\vec{s},\n",
    "\\tag{2}\n",
    "\\label{eq:modularity-max}\n",
    "\\end{equation*}\n",
    "\n",
    "where the modularity matrix $B$ has been introduced as \n",
    "\n",
    "\\begin{equation*}\n",
    "B_{ij} = \\left( A_{ij} - \\frac{g_i g_j}{2m}\\right).\n",
    "\\tag{3}\n",
    "\\label{eq:modularity-matrix}\n",
    "\\end{equation*}\n",
    "\n",
    "Using the conversion $s_i=2x_i-1$, and the fact that $\\sum_{i,j}B_{ij}=0$, the maximization of the modularity [Eq. (3)] can be expressed equivalently as a minimization problem of QUBO form, with QUBO Hamiltonian $H=-\\left(1/m\\right){\\vec{x}}^TB\\vec{x}$, and with QUBO matrix $Q= -B/m$. QUBO matrices like this one can be readily passed to a quantum annealing device such as D-Wave, which will then try to find the optimal bitstring $\\vec{x}$ that encodes the solution to our optimization problem.\n",
    "\n",
    "### Multiple communities ($k>2$) <a name=\"multi_communities\"></a>\n",
    "\n",
    "Let us now turn to the more general problem of community detection with $k>2$ communities. In this case, we have to resort to Eq. (1), but we note that the function $\\delta\\left(c_i,c_j\\right)$ is not necessarily a quadratic binary variable function. In order to formulate the $k$-community detection problem in canonical QUBO form (as required for quantum-native and quantum-inspired annealing), we take the following steps to construct the corresponding QUBO matrix.\n",
    "\n",
    "\n",
    "#### One-Hot Encoding <a name=\"one_hot_encoding\"></a>\n",
    "\n",
    "We use a one-hot-encoding scheme where we set the decision variable $x_{i,c}=1$ if node $i$ belongs to community $c$, and $x_{i,c}=0$ otherwise, i.e., \n",
    "\n",
    "\\begin{equation*}\n",
    "x_{i,c} = \n",
    "\\begin{cases}\n",
    "1 & \\mbox{if node } i \\mbox{ is in community } c \\\\\n",
    "0 & \\mbox{otherwise}\n",
    "\\end{cases}\n",
    "\\tag{4}\n",
    "\\label{eq:one-hot-encoding}\n",
    "\\end{equation*}\n",
    "\n",
    "With this encoding we need $k$ variables per logical node, and the size of the binary decision vector $\\vec{x}$ increases from a vector of length $N$ for the two-community case to $k\\times N$ for the $k$-community case. Specifically, we set $\\vec{x}=\\left(x_{1,1},x_{2,1},\\ldots,x_{N,1},\\ldots,x_{1,k},x_{2,k},\\ldots,x_{N,k}\\right)$.\n",
    "\n",
    "#### Generalized QUBO formulation <a name=\"qubo_formulation\"></a>\n",
    "\n",
    "Accordingly, we can generalize the $k=2$ QUBO problem with Hamiltonian $H=\\vec{x}^T Q \\vec{x}$ to the $k>2$ setting as expressed by a minimization problem for the $k$-community QUBO Hamiltonian $H=-(1/m)\\sum_{c=1}^{k}\\vec{x_{c}}^T B \\vec{x_{c}}$, with every term in the sum describing the community detection problem for a community $c$.\n",
    "\n",
    "Introducing the generalized modularity matrix $\\mathcal{B}$ of size $kN \\times kN$ and block-diagonal form with $B$ along the diagonal as\n",
    "\n",
    "\\begin{equation*}\n",
    "  \\mathcal{B} = \\frac{1}{m}\n",
    "  \\begin{bmatrix}\n",
    "    B & & \\\\\n",
    "    & \\ddots & \\\\\n",
    "    & & B\n",
    "  \\end{bmatrix}  \n",
    "\\tag{5}\n",
    "\\label{eq:modularity-matrix-block}\n",
    "\\end{equation*}\n",
    "\n",
    "we can re-write the $k$-community detection problem as a minimization problem captured by the QUBO Hamiltonian\n",
    "\n",
    "\\begin{equation*}\n",
    "H = -\\vec{x}^T \\mathcal{B} \\vec{x}.\n",
    "\\tag{6}\n",
    "\\label{eq:qubo-general}\n",
    "\\end{equation*}\n",
    "\n",
    "_Constraints_: Since each node $i=1, \\dots, N$ must be in exactly one community $c=1, \\dots, k$, we need to add a constraint term. \n",
    "This type of constraint is well-known from the canonical graph coloring problem where each node must be colored with exactly one color. \n",
    "Formally, it can be written as \n",
    "\n",
    "\\begin{equation*}\n",
    "\\sum_{j=1}^{k}{x_{i,c}} = 1, \n",
    "\\tag{7}\n",
    "\\label{eq:constraint}\n",
    "\\end{equation*}\n",
    "\n",
    "for $i=1, \\dots,N$. This linear constraint can be added to the QUBO problem with a quadratic penalty term, as described by the penalty Hamiltonian\n",
    "\n",
    "\\begin{equation*}\n",
    "H_{P} = \\sum_{i=1}^{N} P_{i} \\left(\\sum_{c=1}^{k} x_{i,c} - 1 \\right)^2, \n",
    "\\tag{8}\n",
    "\\label{eq:penalty}\n",
    "\\end{equation*}\n",
    "\n",
    "with positive prefactors $P_{i}>0$ enforcing the constraints, driving the minimization therefore to valid solutions that satisfy the constraints Eq. (7). \n",
    "\n",
    "To construct a $Q$ matrix for the Eq.(8) penalty term $H_{P} = \\vec{x}^T Q \\vec{x}$, we first re-number the binary decision vector using a single subscript, from 1 to $kN$, as follows:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\vec{x} = (x_{1,1}, x_{2,1}, \\dots , x_{N,1}, \\dots , x_{N, k}) = (x_{1}, x_{2}, \\dots, x_{N}, \\dots, x_{kN})\n",
    "\\tag{9}\n",
    "\\label{eq:re_number}\n",
    "\\end{equation*}\n",
    "\n",
    "By setting $p_{i}$ as a constant $\\alpha$, the penalty term Eq. (8) can be rewritten as:\n",
    "\n",
    "\\begin{equation*}\n",
    "H_P=\\alpha\\left(V\\vec{x}-\\vec{b}\\right)^T\\left(V\\vec{x}-\\vec{b}\\right)\n",
    "\\tag{10}\n",
    "\\label{eq:new_penalty}\n",
    "\\end{equation*}\n",
    "\n",
    "where b is a vector of all ones and $V$ is a $N \\times kN$ matrix:\n",
    "\n",
    "\\begin{equation*}\n",
    "V=\\left[\\begin{matrix}I_N&\\cdots&I_N\\end{matrix}\\right]\n",
    "\\tag{11}\n",
    "\\label{eq:V_matrix}\n",
    "\\end{equation*}\n",
    "\n",
    "where $I_{N}$ is a $N \\times N$ identity matrix, and $Vx-b = 0$ if all constraints are satisfied.\n",
    "\n",
    "We expand Eq. (10) and drop the constant term ${\\vec{b}}^T\\vec{b}$. Using $x_i=x_i^2$, the penalty term can be rewritten as a QUBO Hamiltonian:\n",
    "\n",
    "\\begin{equation*}\n",
    "Q_P = \\alpha \\left(V^TV-2I\\right)\n",
    "\\tag{12}\n",
    "\\label{eq:Q_P}\n",
    "\\end{equation*}\n",
    "\n",
    "where $I$ is a $kN \\times kN$ identity matrix.\n",
    "\n",
    "\n",
    "Combining the modularity QUBO matrix in Eq. (5) and the penalty QUBO matrix in Eq. (12), we have the final QUBO Hamiltonian for $k$-community detection:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\mathrm{H\\ =}{\\vec{x}}^T\\left(-\\mathcal{B}+\\alpha\\left(V^TV-2I\\right)\\right)\\vec{x}\n",
    "\\tag{13}\n",
    "\\label{eq:qubo_k}\n",
    "\\end{equation*}\n",
    "\n",
    "with the penalty coefficient $\\alpha$ enforcing the constraint terms $\\sum_{c=1}^{k}x_{i,c}=1$. The QUBO matrix in Eq. (13) has a size of $kN \\times kN$ which scales with both the number of nodes $N$ in the network and the number of communities $k$, due to the overhead of embedding a $k$-class variable in a binary variable.\n",
    "\n",
    "**The code implementation for the QUBO matrix** can be found in `./src/qubo_community/qubo_matrix_community_sparse`."
   ]
  },
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   "id": "efd99f5a",
   "metadata": {},
   "source": [
    "## Datasets <a name=\"datasets\"></a>\n",
    "The graph datasets used for demonstration here are downloaded from http://networkrepository.com/ under a Creative Commons Attribution-ShareAlike License. Dataset reference: Rossi, Ryan A.  and Ahmed, Nesreen K. (2015) The Network Data Repository with Interactive Graph Analytics and Visualization. AAAI https://networkrepository.com.\n",
    "\n",
    "For illustration, below we display an example for $k=4$ communities with a modularity value of $\\sim 0.42$, as found with D-Wave's `QBSolv` solver for the well-known [Zachary karate club network](https://en.wikipedia.org/wiki/Zachary%27s_karate_club): (figure from author)\n",
    "\n",
    "<img src=\"image/Karate_DwaveQBSolv_4comm_modu0.4198.png\" width=\"500\"/>"
   ]
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   "source": [
    "## List of Key Functions <a name=\"list_functions\"></a>\n",
    "\n",
    "* Script '*src/graph_community.py*': graph data preparation \n",
    "    1. class `CommunityGraphFactory()`: load a local graph file or create a random partition graph\n",
    "    2. function `load_node_labels()`: load the community (or group) labels for nodes in a random partition graph\n",
    "    \n",
    "    \n",
    "* Script '*src/qbsolv_community.py*': QBSolv solver execution\n",
    "    1. class `QbsolvCommunity()`: solve QUBO optimization problem using either the QBSolv classical solver or hybrid solver\n",
    "    2. class `CommunityDetectionSolver()`:  built on top of `qbsolv_community()` with added features to track and save detailed results\n",
    "    \n",
    "    \n",
    "* Script '*src/qubo_community.py*': modularity matrix and QUBO matrix construction\n",
    "    1. function `modularity_mx()`: create a modularity matrix for a NetworkX graph\n",
    "    2. function `qubo_matrix_community_sparse()`: create a QUBO matrix for a community detection task\n",
    "    3. function `qbsolv_response_to_community()`: convert QBSolv results to communty structure results of a graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0af80b5",
   "metadata": {},
   "source": [
    "## Set Up Environment <a name=\"Set_Up_Environment\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "510dee2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "import pandas as pd\n",
    "import collections\n",
    "from collections import defaultdict\n",
    "from itertools import combinations\n",
    "import itertools\n",
    "import math\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import json\n",
    "import os\n",
    "import datetime\n",
    "from networkx.algorithms import community\n",
    "import scipy.sparse as sp\n",
    "import warnings\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa08902",
   "metadata": {},
   "source": [
    "### Helper Functions for Community Detection <a name=\"functions\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "871bdc1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(1, './src')\n",
    "from utils import download_graphs, clean_graph_data\n",
    "from graph_community import CommunityGraphFactory, load_node_labels, draw_graph_community\n",
    "from qbsolv_community import QbsolvCommunity, CommunityDetectionSolver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b3a358d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a output folder to store files\n",
    "if not os.path.exists('output'):\n",
    "    os.makedirs('output')\n",
    "    \n",
    "# get timenow for file naming \n",
    "timenow = str(datetime.datetime.now())[:19]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e330b528",
   "metadata": {},
   "source": [
    "## Download Graph Data <a name=\"download_graph\"></a>\n",
    "\n",
    "In this tutorial, we demonstrate community detection using both real-world graphs and synthetic graphs. We provide code examples below to download real-world graphs from http://networkrepository.com and cleanse the data files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7b41d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a directory to store graph data\n",
    "data_dir = \"./data\"\n",
    "\n",
    "# find graph zip file url from http://networkrepository.com/ \n",
    "graph_url = {'jazz': 'http://nrvis.com/download/data/misc/arenas-jazz.zip',\n",
    "           'dolphins': 'http://nrvis.com/download/data/misc/dolphins.zip',\n",
    "           'LesMiserables': 'http://nrvis.com/download/data/misc/lesmis.zip',\n",
    "           'elegans': 'http://nrvis.com/download/data/misc/celegans_metabolic.zip',\n",
    "           'emailuniv': 'https://nrvis.com/download/data/ia/ia-email-univ.zip',\n",
    "           'cora': 'https://nrvis.com/download/data/labeled/cora.zip'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fdcdcd80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# download graph zip file from http://networkrepository.com/ and store the data under data_dir\n",
    "download_graphs(graph_url, data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb231ae",
   "metadata": {},
   "source": [
    "To cleanse raw data files, we need to manually examine the downloaded graph files: \n",
    "\n",
    " 1. Identify the name of a graph file that contains node-to-node connection and the edge weight data.\n",
    " 2. Open that graph file and count how many header lines are there. We will need to skip the header lines when load the graph data for community detection.\n",
    " 3. Create the `graph_files` dictionary as shown below for graph data cleanse by removing header lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d152829f",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_files = {'Jazz': {'file': 'arenas-jazz.edges', 'lines_to_skip': 1},\n",
    "               'Dolphins': {'file': 'dolphins.mtx', 'lines_to_skip': 2},\n",
    "               'LesMiserables': {'file': 'lesmis.mtx', 'lines_to_skip': 2},\n",
    "               'Elegans': {'file': 'celegans_metabolic.mtx','lines_to_skip': 2},\n",
    "               'Emailuniv': {'file': 'ia-email-univ.mtx', 'lines_to_skip': 2},\n",
    "               'Cora': {'file': 'cora.edges', 'lines_to_skip': 0}\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f6cb45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleanse graph data files: remove header lines and save the graph file to a graph specific directory\n",
    "clean_graph_data(graph_files, data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10215ded",
   "metadata": {},
   "source": [
    "## Prepare a Graph for Community Detection <a name=\"create_graph\"></a>\n",
    "\n",
    "We prepare graph data as an input for community detection via three ways: 1) use NetworkX built-in graphs; 2) load a graph from a local file; 3) create a synthetic graph via NetworkX's random partition graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb199aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# call CommunityGraphFactory for graph data preparation\n",
    "cgf = CommunityGraphFactory(seed=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba6a168d",
   "metadata": {},
   "source": [
    "In order to load a graph from local data files via a name, we create a dictionary `graph_file_dict` with graph names, their local file path, and the delimiter used in the file. \n",
    "\n",
    "`graph_file_dict` is required to have this format: {graph_name: [data_path, delimiter]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac3b0e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_file_dict = {\"Jazz\": [\"./data/Jazz/arenas-jazz.edges\", \",\"], \n",
    "                   \"Dolphins\": [\"./data/Dolphins/dolphins.mtx\", None],\n",
    "                   \"LesMiserables\": [\"./data/LesMiserables/lesmis.mtx\",None],\n",
    "                   \"Elegans\": [\"./data/Elegans/celegans_metabolic.mtx\", None],\n",
    "                   \"Emailuniv\": [\"./data/Emailuniv/ia-email-univ.mtx\", None],\n",
    "                   \"Cora\": [\"./data/Cora/cora.edges\", \",\"]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02ec2a3",
   "metadata": {},
   "source": [
    "### Built-In Graph <a name=\"Built-in_Graph\"></a>\n",
    "\n",
    "This code example shows how to create the Zachary's karate club graph using NetworkX's built-in graph function, and draws the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "936495dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using networkx graph\n",
    "graph_name = \"Zachary\"\n",
    "graph_zachary = nx.karate_club_graph()\n",
    "draw_graph_community(graph_zachary, [list(range(graph_zachary.number_of_nodes()))], color_map = 'Dark2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e550d7b6",
   "metadata": {},
   "source": [
    "### Graph from a Local Data File <a name=\"local_graph\"></a>\n",
    "This code example shows how to create a graph through loading a local data file listed in `graph_file_dict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9970bfc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 62\n",
      "Number of edges: 159\n",
      "Average degree:   5.1290\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load a graph from local files defined in 'graph_file_dict'\n",
    "graph_name = \"Dolphins\"\n",
    "graph_dolphins = cgf.load_graph(graph_name, graph_file_dict)\n",
    "draw_graph_community(graph_dolphins, [list(range(graph_dolphins.number_of_nodes()))], color_map = 'Dark2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7686886",
   "metadata": {},
   "source": [
    "### Random Partition Graph <a name=\"random_graph\"></a>\n",
    "\n",
    "This code example shows how to create a synthetic graph using NetworkX's random partition graph. The [NetworkX random partition graph](https://networkx.org/documentation/stable/reference/generated/networkx.generators.community.random_partition_graph.html) has pre-defined community structure; therefore we can evaluate the community assignments found by QBSolve against this ground-truth label data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4923fad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterating graph generation with random seed 2\n",
      "iterating graph generation with random seed 3\n",
      "iterating graph generation with random seed 4\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 60\n",
      "Number of edges: 157\n",
      "Average degree:   5.2333\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a random partition graph\n",
    "graph_name = \"random\"\n",
    "total_nodes = 60 # the number of nodes in a graph\n",
    "num_comm = 3 # the number of communities\n",
    "prob_in = 0.2 # probability of intra-community edge connection\n",
    "prob_out = 0.02 # probability of inter-community edge connection\n",
    "\n",
    "graph_random = cgf.create_random_graph(total_nodes, num_comm, prob_in, prob_out)\n",
    "\n",
    "draw_graph_community(graph_random, [list(range(graph_random.number_of_nodes()))], color_map = 'Dark2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acd1950b",
   "metadata": {},
   "source": [
    "The code example below demonstrates how to calculate the modularity value using the pre-defined community labels for a random partition graph. This modularity value can be used as a target baseline value to evaluate against with the QBSolv solution for community detection. The code example also shows how to draw a network with nodes colored by their community labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "313a197d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The modularity value for the pre-defined labels is 0.4843\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load nodes' community labels and calculate its modularity value\n",
    "file_path = f\"./data/synthetic/node_labels_{total_nodes}_{num_comm}_{prob_in}_{prob_out}.node_labels\"\n",
    "label_nodes_dict, label_community, labels_array = load_node_labels(file_path)\n",
    "modu_labels= community.modularity(graph_random, label_community)\n",
    "print(f\"The modularity value for the pre-defined labels is {round(modu_labels,4)}\")\n",
    "\n",
    "# plot a random partition graph with their nodes colored by label groups\n",
    "draw_graph_community(graph_random, label_community, color_map = 'Dark2')\n",
    "savefig_name = f\"{timenow}_{graph_name}_labels_{num_comm}_comm_modu_{round(modu_labels, 4)}.png\"\n",
    "plt.savefig(os.path.join(\"./output\", savefig_name), dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbf38a1",
   "metadata": {},
   "source": [
    "## QBSolv for Community Detection <a name=\"QBSolv_run\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c038d8",
   "metadata": {},
   "source": [
    "`QBSolv` is a hybrid solver that decomposes large QUBO problems into smaller QUBO sub-problems. The sub-problems are then solved individually, using either QBSolv on the D-Wave QPUs (hereafter referred to as hybrid solver), or QBSolv with the classical Tabu solver alone (hereafter referred to as classical solver). The solution to the original QUBO problem is then constructed by stitching together the results of the smaller sub-problems. More technical details can be found in the [D-Wave QBSolv whitepaper](https://docs.ocean.dwavesys.com/projects/qbsolv/en/latest/_downloads/bd15a2d8f32e587e9e5997ce9d5512cc/qbsolv_techReport.pdf) and the [D-Wave QBSolv documentation](https://docs.ocean.dwavesys.com/projects/qbsolv/en/latest/).\n",
    "\n",
    "The code examples below demonstrate how to solve community detection problems using QBSolv."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d42a9a",
   "metadata": {},
   "source": [
    "### Set Community Parameters for a Graph <a name=\"param_graph\"></a>\n",
    "\n",
    "We choose a graph and set the number of communities to detect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b602dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graph parameters\n",
    "## Option 1: use a NetworkX built-in graph that was created in the previous section\n",
    "k = 4\n",
    "graph_name = \"Zachary\"\n",
    "nx_G = graph_zachary\n",
    "\n",
    "## Option 2: use a real-world graph that was created in the previous section\n",
    "# k = 5\n",
    "# graph_name = 'Dolphins'\n",
    "# nx_G = graph_dolphins\n",
    "\n",
    "## Option 3: use a synthetic graph that was created in the previous section\n",
    "# k = 3\n",
    "# graph_name = \"random\"\n",
    "# nx_G = graph_random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3cf17f1",
   "metadata": {},
   "source": [
    "### Set Hyperparameters for QBSolv <a name=\"hyperparam_QBSolv\"></a>\n",
    "We configure the QBSolv settings. Remember to set the S3 bucket name to your S3 bucket with a name beginning with \"amazon-braket-\"; you may have created such a bucket when you were onboarded to Amazon Braket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5dfb5115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S3 path for saving QBSolv results: amazon-braket-8c8bf2e4cf2e/Zachary_k4_sl40_nr1_s1000_seed1\n"
     ]
    }
   ],
   "source": [
    "# QBSolv parameters\n",
    "solver_limit = 40 # the maximum number of variables (n) for sub-QUBOs\n",
    "num_repeats = 1 # the maximum iterations to repeat QBSolv solver execution to discover a new best solution\n",
    "num_reads = 1000 # how many times the annealing is performed\n",
    "seed = 1 # random seed\n",
    "alpha = 5 # the penalty coefficient to enforce assigning only one community to each node\n",
    "\n",
    "# for QBSolv hybrid only\n",
    "device_arn = 'arn:aws:braket:::device/qpu/d-wave/Advantage_system4' # D-Wave QPU Device ARN\n",
    "my_prefix = f\"{graph_name}_k{k}_sl{solver_limit}_nr{num_repeats}_s{num_reads}_seed{seed}\" #the folder name in the S3 braket bucket\n",
    "my_bucket = f\"amazon-braket-your-bucket-name-here\" # rename 'amazon-braket-your-bucket-name-here' with the name of your amazon S3 Braket bucket\n",
    "s3_folder = (my_bucket, my_prefix)\n",
    "\n",
    "print(f\"S3 path for saving QBSolv results: {my_bucket}/{my_prefix}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12fe9872",
   "metadata": {},
   "source": [
    "### Initialize QbsolvCommunity Class <a name=\"init_qbsolv\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dbef05ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "qbsolv_comm = QbsolvCommunity(nx_G, solver_limit, num_repeats, num_reads, seed, alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976447af",
   "metadata": {},
   "source": [
    "### Execute QBSolv Classical Solver <a name=\"run_qbsolv_classical\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67877e7",
   "metadata": {},
   "source": [
    "The code example below shows how to solve community detection using the QBSolv **classical solver**.\n",
    "The QBSolv solver response presents solution values for the decision vector Eq. (9) in columns indexed by 0, 1, ..., kN-1. For the Zachary’s karate club graph there are $N=34$ nodes and we want to detect $k=4$ communities, so the solver response has $kN = 136$ columns.\n",
    "\n",
    "Each row in the response represents one possible solution for the decision vector Eq. (9). Not all solutions are optimal, and the number of returned solutions depends on the graph and QBSolv hyperparameter settings. We only choose a solution with the lowest energy as the answer for community assignment.\n",
    "\n",
    "For the Zachary’s karate club, the highest modularity reported in literature is 0.41979, seen in Ref. [5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538). The QBSolv classical solver can achieve this modularity with the right QBSolv settings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0386ae59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the QUBO matrix in dictionary format for 4-community is 5032\n",
      "Mode: Classical, time spent is 0.36 seconds for 1 repetitions\n",
      "    0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 135    energy num_oc.\n",
      "0   1  1  1  1  0  0  0  1  0  0  0  1  1  1  0  0  0 ...   0  -5.41979       1\n",
      "1   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0  -5.41979       1\n",
      "2   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0  -5.41979       1\n",
      "3   0  0  0  0  0  0  0  0  1  1  0  0  0  0  1  1  0 ...   0  -5.41979       1\n",
      "4   1  1  1  1  0  0  0  1  0  1  0  1  1  1  0  0  0 ...   0 -5.418803       1\n",
      "5   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.418803       1\n",
      "6   0  0  0  0  1  1  1  0  0  0  1  0  0  0  0  0  1 ...   0 -5.418803       1\n",
      "7   0  0  0  0  1  1  1  0  0  0  1  0  0  0  0  0  1 ...   0 -5.418803       1\n",
      "8   1  1  1  1  0  0  0  1  0  0  0  1  1  1  0  0  0 ...   0 -5.417406       1\n",
      "9   0  0  0  0  0  0  0  0  1  0  0  0  0  0  1  1  0 ...   0 -5.417242       1\n",
      "10  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.417242       1\n",
      "11  0  0  0  0  1  1  1  0  0  0  1  0  0  0  0  0  1 ...   0 -5.417242       1\n",
      "12  0  0  0  0  0  0  0  0  1  0  0  0  0  0  1  1  0 ...   0   -5.4119       1\n",
      "13  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  0  0 ...   0 -5.409352       1\n",
      "14  0  0  0  0  1  1  1  0  0  0  1  0  0  0  0  0  1 ...   0 -5.402038       1\n",
      "15  0  0  0  0  0  0  0  0  1  0  0  0  0  0  1  1  0 ...   0 -5.402038       1\n",
      "16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.400394       1\n",
      "17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.395874       1\n",
      "18  0  0  0  0  0  0  0  0  1  0  0  0  0  0  1  1  0 ...   0 -5.395874       1\n",
      "19  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.395874       1\n",
      "['BINARY', 20 rows, 20 samples, 136 variables]\n",
      "The best solution's modularity value is: 0.4197896120973035\n"
     ]
    }
   ],
   "source": [
    "comm_classical, _ = qbsolv_comm.solve_classical(k)\n",
    "print(f\"The best solution's modularity value is: {comm_classical['modularity']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a280d45",
   "metadata": {},
   "source": [
    "`qbsolv_comm` calls a function 'qbsolv_response_to_community' to extract communities from QBSolv responses and calculate its modularity. Here we print out the extracted community result, and visualize the graph partition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "88c5b76f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'modularity': 0.4197896120973035, 'num_comm': 4, 'comm': [{0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21}, {4, 5, 6, 10, 16}, {32, 33, 8, 9, 14, 15, 18, 20, 22, 26, 29, 30}, {23, 24, 25, 27, 28, 31}]}\n"
     ]
    }
   ],
   "source": [
    "print(comm_classical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3ddccaf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We save the QBSolv response to a local pickle file\n",
    "response_file = f\"{timenow}_DwaveQBSolv_Classical_response_{my_prefix}.p\"\n",
    "pickle.dump(comm_classical, open('./output/'+response_file, \"wb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9f327b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved figure to ./output: 2021-11-08 17:58:15_Zachary_DwaveQBSolv_classical_K4_Modu_0.4198.png\n"
     ]
    }
   ],
   "source": [
    "# We plot a graph colored with communities and save it as an image\n",
    "draw_graph_community(nx_G, comm_classical['comm'], color_map = 'Dark2')\n",
    "savefig_name = f\"{timenow}_{graph_name}_DwaveQBSolv_classical_K{k}_Modu_{round(comm_classical['modularity'], 4)}.png\"\n",
    "plt.savefig(os.path.join(\"./output\", savefig_name), dpi=150)\n",
    "plt.show()\n",
    "print(f\"Saved figure to ./output: {savefig_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d8a674",
   "metadata": {},
   "source": [
    "### Execute QBSolv Hybrid Solver <a name=\"run_qbsolv_hybrid\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eed4cca",
   "metadata": {},
   "source": [
    "Now we demonstrate how to use the QBSolv **hybrid solver** to solve community detection problems.\n",
    "\n",
    "Similar to the classical solver, the QBSolv solver response presents solution values for the decision vector Eq. (9) in columns indexed by 0, 1, ..., kN-1. For the Zachary’s karate club graph there are $N=34$ nodes and we want to detect $k=4$ communities, so the solver response has $kN = 136$ columns.\n",
    "\n",
    "Each row in the response represents one possible solution for the decision vector Eq. (9). Not all solutions are optimal, and the number of returned solutions depends on the graph and QBSolv hyperparameter settings. We only choose a solution with the lowest energy as the answer for community assignment.\n",
    "\n",
    "For the Zachary’s karate club, the highest modularity reported in literature is 0.41979, seen in Ref. [5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538). The QBSolv hybrid solver can achieve this modularity with the right QBSolv settings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a32cdf5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[91mWARNING:\u001b[0m Additional cost for using D-Wave QPU. Please evaluate potential cost before executing this QBSolv hybrid job\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the QUBO matrix in dictionary format for 4-community is 5032\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Continue to execute QBSolv Hybrid job: Y or N? Y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mode: Hybrid, time spent is 49.79 seconds for 1 repeats\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 135    energy num_oc.\n",
      "0  1  1  1  1  0  0  0  1  0  0  0  1  1  1  0  0  0 ...   1  -5.41979       1\n",
      "1  1  1  1  1  0  0  0  1  0  0  0  1  1  1  0  0  0 ...   1  -5.41979       1\n",
      "2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.395874       1\n",
      "3  1  1  1  1  0  0  0  1  0  1  0  1  1  1  0  0  0 ...   0 -5.395874       1\n",
      "4  1  1  1  1  0  0  0  1  0  0  0  0  0  1  0  0  0 ...   1 -5.391437       1\n",
      "['BINARY', 5 rows, 5 samples, 136 variables]\n",
      "Modularity value is: 0.4197896120973035\n"
     ]
    }
   ],
   "source": [
    "comm_hybrid, _ = qbsolv_comm.solve_hybrid(k, s3_folder, device_arn)\n",
    "print(f\"Modularity value is: {comm_hybrid['modularity']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853372bd",
   "metadata": {},
   "source": [
    "As in the previous case, we print out the extracted community, save the response to a local file, and visualize the graph partition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc428a03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'modularity': 0.4197896120973035, 'num_comm': 4, 'comm': [{0, 1, 2, 3, 7, 11, 12, 13, 17, 19, 21}, {4, 5, 6, 10, 16}, {32, 33, 8, 9, 14, 15, 18, 20, 22, 26, 29, 30}, {23, 24, 25, 27, 28, 31}]}\n"
     ]
    }
   ],
   "source": [
    "print(comm_hybrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2e41278c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We save the QBSolv response to a local pickle file\n",
    "response_file = f\"{timenow}_DwaveQBSolv_Hyrid_response_{my_prefix}.p\"\n",
    "pickle.dump(comm_hybrid, open('./output/'+response_file, \"wb\" ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae4d00ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved figure to ./output: 2021-11-08 17:58:15_Zachary_DwaveQBSolv_hybrid_4_comm_modu_0.4198.png\n"
     ]
    }
   ],
   "source": [
    "# We plot a graph colored with communities and save it as an image\n",
    "draw_graph_community(nx_G, comm_hybrid['comm'], color_map = 'Dark2')\n",
    "savefig_name = f\"{timenow}_{graph_name}_DwaveQBSolv_hybrid_{k}_comm_modu_{round(comm_hybrid['modularity'], 4)}.png\"\n",
    "plt.savefig(os.path.join(\"./output\", savefig_name), dpi=150)\n",
    "plt.show()\n",
    "print(f\"Saved figure to ./output: {savefig_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fe4771",
   "metadata": {},
   "source": [
    "## Community Detection for Synthetic Graphs and Real-World Graphs <a name=\"Supplementary\"></a>\n",
    "The reason we want to study both synthetic graphs and real-world graphs is to get a holistic view of how good the QBSolv community detection method is:\n",
    "   1. The community results of real-world graphs can be evaluated against some of the published results, for example in Ref. [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538)].\n",
    "   2. Synthetic graphs have ground-truth community labels; therefore we can assess the QBSolv solution quality by comparing to the labeled data. In addition, we can study the impact of a specific graph property on QBSolv solution quality, by varying the graph size, number of communities, probability of edges within communities, and probability of edges between communities. \n",
    "   \n",
    "The code examples below demonstrate community detection using synthetic graphs with different graph configurations and using real-world graphs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9103169",
   "metadata": {},
   "source": [
    "### Study a Set of Synthetic Graphs <a name=\"random_graph_study\"></a>\n",
    "\n",
    "Because synthetic graphs have ground-truth labels for node communities, we can calculate the corresponding modularity value and use it as a target baseline value to evaluate against with the community solution found by QBSolv. By studying the difference between QBSolv solution and grounth truth labels for a various types of synthetic graphs, we can systematically assess the QBSolv solution performance in terms of accuracy, generalizability, and scalability.\n",
    "\n",
    "The code examples below demonstrate community detection for synthetic graphs by varying graph properties in terms of graph size (num of nodes), number of communities (k value), community structure (ratio of prob_in/prob_out), and graph density (value of prob_in, prob_out)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c01621a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a list of random partition graphs with a config dictionary: [{N, K, p_in, p_out}, ...]\n",
    "random_graph_config_list = [{'nodes': 60, 'k': 3, 'prob_in': 0.2, 'prob_out': 0.02},\n",
    "                            {'nodes': 100, 'k': 3, 'prob_in': 0.2, 'prob_out': 0.02},\n",
    "                            {'nodes': 500, 'k': 3, 'prob_in': 0.2, 'prob_out': 0.02},\n",
    "                            {'nodes': 1000, 'k': 4, 'prob_in': 0.2, 'prob_out': 0.02},\n",
    "                            {'nodes': 1000, 'k': 4, 'prob_in': 0.1, 'prob_out': 0.02},\n",
    "                           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fe029263",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iterating graph generation with random seed 2\n",
      "iterating graph generation with random seed 3\n",
      "iterating graph generation with random seed 4\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 60\n",
      "Number of edges: 157\n",
      "Average degree:   5.2333\n",
      "The size of the QUBO matrix in dictionary format for 3-community is 11160\n",
      "Mode: Classical, time spent is 0.08 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 179    energy num_oc.\n",
      "0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 ...   0 -5.489492       2\n",
      "['BINARY', 1 rows, 2 samples, 180 variables]\n",
      "Modularity from QBSolv with 3 communities is 0.4895\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_random_60_3_0.2_0.02_2021-11-08_17:59:51_sl100_rp1_shot1000_seed1.csv\n",
      "Modularity for ground truth labels with 3 communities is 0.4843\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 100\n",
      "Number of edges: 360\n",
      "Average degree:   7.2000\n",
      "The size of the QUBO matrix in dictionary format for 3-community is 30600\n",
      "Mode: Classical, time spent is 0.26 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 299    energy num_oc.\n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.507265       2\n",
      "1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.507265       1\n",
      "2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...   0 -5.503908       1\n",
      "['BINARY', 3 rows, 4 samples, 300 variables]\n",
      "Modularity from QBSolv with 3 communities is 0.5073\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_random_100_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_random_100_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_random_100_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_random_100_3_0.2_0.02_2021-11-08_17:59:51_sl100_rp1_shot1000_seed1.csv\n",
      "Modularity for ground truth labels with 3 communities is 0.5073\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 500\n",
      "Number of edges: 10035\n",
      "Average degree:  40.1400\n",
      "The size of the QUBO matrix in dictionary format for 3-community is 753000\n",
      "Mode: Classical, time spent is 3.97 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 1499    energy num_oc.\n",
      "0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 ...    0 -5.502694       2\n",
      "['BINARY', 1 rows, 2 samples, 1500 variables]\n",
      "Modularity from QBSolv with 3 communities is 0.5027\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_random_500_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_random_500_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_random_500_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_random_500_3_0.2_0.02_2021-11-08_17:59:52_sl100_rp1_shot1000_seed1.csv\n",
      "Modularity for ground truth labels with 3 communities is 0.5027\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 1000\n",
      "Number of edges: 32309\n",
      "Average degree:  64.6180\n",
      "The size of the QUBO matrix in dictionary format for 4-community is 4012000\n",
      "Mode: Classical, time spent is 22.68 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 3999    energy num_oc.\n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 ...    0 -5.521673       2\n",
      "['BINARY', 1 rows, 2 samples, 4000 variables]\n",
      "Modularity from QBSolv with 4 communities is 0.5217\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_random_1000_4_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_random_1000_4_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_random_1000_4_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_random_1000_4_0.2_0.02_2021-11-08_17:59:57_sl100_rp1_shot1000_seed1.csv\n",
      "Modularity for ground truth labels with 4 communities is 0.5217\n",
      "Name: stochastic_block_model\n",
      "Type: Graph\n",
      "Number of nodes: 1000\n",
      "Number of edges: 19724\n",
      "Average degree:  39.4480\n",
      "The size of the QUBO matrix in dictionary format for 4-community is 4012000\n",
      "Mode: Classical, time spent is 23.42 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 3999   energy num_oc.\n",
      "0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 ...    0 -5.37646       2\n",
      "['BINARY', 1 rows, 2 samples, 4000 variables]\n",
      "Modularity from QBSolv with 4 communities is 0.3765\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_random_1000_4_0.1_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_random_1000_4_0.1_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_random_1000_4_0.1_0.02_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_random_1000_4_0.1_0.02_2021-11-08_18:00:22_sl100_rp1_shot1000_seed1.csv\n",
      "Modularity for ground truth labels with 4 communities is 0.3765\n"
     ]
    }
   ],
   "source": [
    "# initialize the class of CommunityGraphFactory\n",
    "cgf = CommunityGraphFactory(seed=1)\n",
    "\n",
    "# Community detection for a list of random graphs\n",
    "\n",
    "rand_graph_results = pd.DataFrame()\n",
    "rand_node_results = []\n",
    "\n",
    "for graph_i in random_graph_config_list:\n",
    "    assert len(graph_i) == 4, f\"Wrong config for random graphs: {graph_i}\"\n",
    "    total_nodes = graph_i['nodes']\n",
    "    num_comm = graph_i['k']\n",
    "    prob_in = graph_i['prob_in']\n",
    "    prob_out = graph_i['prob_out']\n",
    "    \n",
    "    # create a name for random graph\n",
    "    graph_name = f\"random_{total_nodes}_{num_comm}_{prob_in}_{prob_out}\"\n",
    "    \n",
    "    # create a random graph based on a configuration\n",
    "    graph = cgf.create_random_graph(total_nodes, num_comm, prob_in, prob_out)\n",
    "    \n",
    "    # load the community labels for the random graph node. \n",
    "    # Note the 'file_path' is a fixed pattern, seen in function 'create_random_graph()' from 'graph_community' script\n",
    "    file_path = f\"./data/synthetic/node_labels_{total_nodes}_{num_comm}_{prob_in}_{prob_out}.node_labels\"\n",
    "    label_nodes_dict, label_community, labels_array = load_node_labels(file_path)\n",
    "    modu_labels = community.modularity(graph, label_community)\n",
    "    \n",
    "    # run community detection using QBSolv\n",
    "    community_solver = CommunityDetectionSolver(graph_name, graph, num_comm, solver_limit=100, num_repeats=1, num_reads=1000, seed=1, alpha=5, mode='classical')\n",
    "    result, node_track = community_solver.run_community_detection(save=True)\n",
    "    \n",
    "    # append node-level results to the list of rand_node_results\n",
    "    rand_node_results.append(node_track)\n",
    "    \n",
    "    # print modularity from the community labels, this is the baseline to evalaute the qbsolv solution\n",
    "    print(f\"Modularity for ground truth labels with {len(label_nodes_dict.keys())} communities is {round(modu_labels, 4)}\")\n",
    "    \n",
    "    # add random graph specific data into the solution\n",
    "    result['prob_in'].append(prob_in)\n",
    "    result['prob_out'].append(prob_out)\n",
    "    result['modu_labels'].append(modu_labels)\n",
    "    \n",
    "    # convert result into dataframe\n",
    "    df_result_ = pd.DataFrame.from_dict(result)\n",
    "    rand_graph_results = rand_graph_results.append(df_result_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f860b00f",
   "metadata": {},
   "source": [
    "#### We log solution results into a DataFrame with these types of information:\n",
    "- Graph related data: graph_name, total_nodes, num_edge, num_comm, prob_in, prob_out (prob_in and prob_out are only for random partition graph)\n",
    "- QBsolv solver setting values: solver_limit, num_repeats, num_reads, seed, alpha, mode (classical solver or hybrid solver), s3_folder (only for hybrid solver, default is N/A)\n",
    "- Modularity values: modu_qbsolv (QBSolv solution's modularity) and modu_labels (the graph's community-label-based modularity, only for random partition graph)\n",
    "- QBSolv execution wall time: wall_time_s\n",
    "\n",
    "\n",
    "Note: When the graph size is small (e.g., less than 100 nodes), the NetworkX pre-defined labels for random partition graphs do not necessarily preserve a maximum modularity due to a finite size effect on random partitioning, which is why we could find that QBSolv identifies even slightly better community assignments than the pre-defined labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae9e1e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>graph_name</th>\n",
       "      <th>total_nodes</th>\n",
       "      <th>num_edge</th>\n",
       "      <th>num_comm</th>\n",
       "      <th>modu_qbsolv</th>\n",
       "      <th>num_comm_qbsolv</th>\n",
       "      <th>wall_time_s</th>\n",
       "      <th>solver_limit</th>\n",
       "      <th>num_repeats</th>\n",
       "      <th>num_reads</th>\n",
       "      <th>seed</th>\n",
       "      <th>alpha</th>\n",
       "      <th>mode</th>\n",
       "      <th>s3_folder</th>\n",
       "      <th>prob_in</th>\n",
       "      <th>prob_out</th>\n",
       "      <th>modu_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_60_3_0.2_0.02</td>\n",
       "      <td>60</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0.489492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.08</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.484340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_100_3_0.2_0.02</td>\n",
       "      <td>100</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>0.507265</td>\n",
       "      <td>3</td>\n",
       "      <td>0.27</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.507265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_500_3_0.2_0.02</td>\n",
       "      <td>500</td>\n",
       "      <td>10035</td>\n",
       "      <td>3</td>\n",
       "      <td>0.502694</td>\n",
       "      <td>3</td>\n",
       "      <td>4.31</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.502694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_1000_4_0.2_0.02</td>\n",
       "      <td>1000</td>\n",
       "      <td>32309</td>\n",
       "      <td>4</td>\n",
       "      <td>0.521673</td>\n",
       "      <td>4</td>\n",
       "      <td>24.11</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.521673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_1000_4_0.1_0.02</td>\n",
       "      <td>1000</td>\n",
       "      <td>19724</td>\n",
       "      <td>4</td>\n",
       "      <td>0.376460</td>\n",
       "      <td>4</td>\n",
       "      <td>24.79</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.376460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               graph_name  total_nodes  num_edge  num_comm  modu_qbsolv  \\\n",
       "0    random_60_3_0.2_0.02           60       157         3     0.489492   \n",
       "0   random_100_3_0.2_0.02          100       360         3     0.507265   \n",
       "0   random_500_3_0.2_0.02          500     10035         3     0.502694   \n",
       "0  random_1000_4_0.2_0.02         1000     32309         4     0.521673   \n",
       "0  random_1000_4_0.1_0.02         1000     19724         4     0.376460   \n",
       "\n",
       "   num_comm_qbsolv  wall_time_s  solver_limit  num_repeats  num_reads  seed  \\\n",
       "0                3         0.08           100            1       1000     1   \n",
       "0                3         0.27           100            1       1000     1   \n",
       "0                3         4.31           100            1       1000     1   \n",
       "0                4        24.11           100            1       1000     1   \n",
       "0                4        24.79           100            1       1000     1   \n",
       "\n",
       "   alpha       mode s3_folder  prob_in  prob_out  modu_labels  \n",
       "0      5  classical       N/A      0.2      0.02     0.484340  \n",
       "0      5  classical       N/A      0.2      0.02     0.507265  \n",
       "0      5  classical       N/A      0.2      0.02     0.502694  \n",
       "0      5  classical       N/A      0.2      0.02     0.521673  \n",
       "0      5  classical       N/A      0.1      0.02     0.376460  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# graph-level results \n",
    "rand_graph_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bfd69c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'graphs': [('graph_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.gpickle', <networkx.classes.graph.Graph object at 0x7f215cf3d050>)], 'responses': [('response_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p', SampleSet(rec.array([([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], -5.48949247, 2)],\n",
      "          dtype=[('sample', 'i1', (180,)), ('energy', '<f8'), ('num_occurrences', '<i8')]), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179], {}, 'BINARY'))], 'communities': [('modu_random_60_3_0.2_0.02_QBS_sl100_rp1_shot1000_seed1.p', {'modularity': 0.4894924743397281, 'num_comm': 3, 'comm': [{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}, {19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40}, {27, 32, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59}]})]}\n"
     ]
    }
   ],
   "source": [
    "# node-level results for one graph\n",
    "print(rand_node_results[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc1de72",
   "metadata": {},
   "source": [
    "### Study a Set of Real-World Graphs <a name=\"real_graph_study\"></a>\n",
    "\n",
    "We can also run QBSolv community detection for real-world networks and assess solution performance by comparing to published results. Our experimental results of real-world graphs listed in `graph_file_dict` are on par with results reported in [Negre et. al. work](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227538). \n",
    "\n",
    "The code examples below demonstrate community detection for different real-world graphs and different number of communities to detect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9fb7dade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Jazz': ['./data/Jazz/arenas-jazz.edges', ','],\n",
       " 'Dolphins': ['./data/Dolphins/dolphins.mtx', None],\n",
       " 'LesMiserables': ['./data/LesMiserables/lesmis.mtx', None],\n",
       " 'Elegans': ['./data/Elegans/celegans_metabolic.mtx', None],\n",
       " 'Emailuniv': ['./data/Emailuniv/ia-email-univ.mtx', None],\n",
       " 'Cora': ['./data/Cora/cora.edges', ',']}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the existing local graph data\n",
    "graph_file_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5f2568d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a list of local graph files and the number of communities to detect: [{name, num of communities}, ...]\n",
    "local_graph_list = [{'name': 'Jazz', 'num_comm': 2},\n",
    "                    {'name': 'Jazz', 'num_comm': 3},\n",
    "                    {'name': 'Jazz', 'num_comm': 4},\n",
    "                    {'name': 'Dolphins', 'num_comm': 5}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21c1c4f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 198\n",
      "Number of edges: 2742\n",
      "Average degree:  27.6970\n",
      "The size of the QUBO matrix in dictionary format for 2-community is 78804\n",
      "Mode: Classical, time spent is 0.42 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 395    energy num_oc.\n",
      "0  0  0  0  0  0  0  1  1  1  1  1  0  0  0  0  1  0 ...   0 -5.305469       2\n",
      "['BINARY', 1 rows, 2 samples, 396 variables]\n",
      "Modularity from QBSolv with 2 communities is 0.3055\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_Jazz_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_Jazz_2021-11-08_18:00:47_sl100_rp1_shot1000_seed1.csv\n",
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 198\n",
      "Number of edges: 2742\n",
      "Average degree:  27.6970\n",
      "The size of the QUBO matrix in dictionary format for 3-community is 118800\n",
      "Mode: Classical, time spent is 1.35 seconds for 1 repetitions\n",
      "    0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 593    energy num_oc.\n",
      "0   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.444237       2\n",
      "1   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.443976       1\n",
      "2   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.443843       1\n",
      "3   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   1 -5.443602       1\n",
      "4   0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   0 -5.443007       1\n",
      "5   0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   1 -5.442804       1\n",
      "6   0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   0 -5.442624       1\n",
      "7   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.442527       1\n",
      "8   0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   0 -5.442488       1\n",
      "9   0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   1 -5.441933       1\n",
      "10  0  0  0  0  0  0  1  1  0  1  0  0  0  0  0  1  0 ...   0 -5.441812       1\n",
      "11  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.441756       1\n",
      "12  0  0  0  0  0  0  1  0  0  1  0  0  0  0  0  1  0 ...   0 -5.441026       1\n",
      "13  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.440785       1\n",
      "14  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  1  0 ...   0  -5.44057       1\n",
      "15  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   0 -5.440526       1\n",
      "16  0  0  0  0  0  0  1  1  0  0  0  0  0  0  0  1  0 ...   0 -5.440366       1\n",
      "17  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  1  0 ...   0 -5.440154       1\n",
      "18  0  0  0  0  0  0  1  1  0  1  0  0  0  0  0  1  0 ...   1 -5.438803       1\n",
      "19  0  0  0  0  0  0  0  1  0  0  1  0  0  0  0  1  0 ...   1 -5.438005       1\n",
      "['BINARY', 20 rows, 21 samples, 594 variables]\n",
      "Modularity from QBSolv with 3 communities is 0.4442\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_Jazz_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_Jazz_2021-11-08_18:00:47_sl100_rp1_shot1000_seed1.csv\n",
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 198\n",
      "Number of edges: 2742\n",
      "Average degree:  27.6970\n",
      "The size of the QUBO matrix in dictionary format for 4-community is 159192\n",
      "Mode: Classical, time spent is 1.76 seconds for 1 repetitions\n",
      "    0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 791    energy num_oc.\n",
      "0   1  1  1  1  1  1  0  0  1  0  1  1  1  1  1  0  1 ...   0 -5.440577       1\n",
      "1   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.439151       1\n",
      "2   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0  -5.43888       1\n",
      "3   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   1 -5.438034       1\n",
      "4   1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1 ...   0 -5.437752       1\n",
      "5   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   1 -5.436951       1\n",
      "6   1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1 ...   0 -5.436798       1\n",
      "7   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.436573       1\n",
      "8   1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.435951       1\n",
      "9   1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1 ...   0 -5.435058       1\n",
      "10  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.434898       1\n",
      "11  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1 ...   0 -5.433833       1\n",
      "12  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   1 -5.432033       1\n",
      "13  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.431846       1\n",
      "14  1  1  1  1  1  1  0  1  1  1  1  1  1  1  1  0  1 ...   1 -5.431032       1\n",
      "15  1  1  0  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   1 -5.430147       1\n",
      "16  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   1 -5.430143       1\n",
      "17  1  1  1  1  1  1  0  0  1  1  1  1  1  1  1  0  1 ...   0 -5.425691       1\n",
      "18  1  1  1  1  1  1  1  0  1  1  1  1  1  1  1  0  1 ...   0 -5.425034       1\n",
      "19  1  1  1  1  1  1  1  0  1  0  1  1  1  1  1  0  1 ...   0 -5.423718       1\n",
      "['BINARY', 20 rows, 20 samples, 792 variables]\n",
      "Modularity from QBSolv with 4 communities is 0.4406\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_Jazz_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_Jazz_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_Jazz_2021-11-08_18:00:49_sl100_rp1_shot1000_seed1.csv\n",
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 62\n",
      "Number of edges: 159\n",
      "Average degree:   5.1290\n",
      "The size of the QUBO matrix in dictionary format for 5-community is 20460\n",
      "Mode: Classical, time spent is 0.37 seconds for 1 repetitions\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 309    energy num_oc.\n",
      "0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  1  0  1 ...   0 -5.526799       1\n",
      "1  0  0  0  1  1  0  0  0  1  0  0  1  0  0  0  1  0 ...   0 -5.526799       1\n",
      "2  0  0  0  1  1  0  0  0  1  0  0  1  0  0  0  1  0 ...   0 -5.526463       1\n",
      "3  1  0  1  0  0  0  0  0  0  0  1  0  0  0  0  0  0 ...   0 -5.526463       1\n",
      "4  0  1  0  0  0  1  1  1  0  1  0  0  0  1  0  0  0 ...   1 -5.522052       1\n",
      "5  0  1  0  0  0  1  1  1  0  1  0  0  0  1  0  0  0 ...   0 -5.520055       1\n",
      "6  0  0  0  1  0  0  0  0  1  0  0  0  0  0  1  0  1 ...   0 -5.486215       1\n",
      "7  0  1  0  1  0  0  0  1  1  0  0  0  0  0  0  0  0 ...   1 -5.484494       1\n",
      "['BINARY', 8 rows, 8 samples, 310 variables]\n",
      "Modularity from QBSolv with 4 communities is 0.5268\n",
      "Writing to disk: ./output/2021-11-08/graphs/graph_Dolphins_QBS_sl100_rp1_shot1000_seed1.gpickle\n",
      "Writing to disk: ./output/2021-11-08/responses/response_Dolphins_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/communities/modu_Dolphins_QBS_sl100_rp1_shot1000_seed1.p\n",
      "Writing to disk: ./output/2021-11-08/result_Dolphins_2021-11-08_18:00:51_sl100_rp1_shot1000_seed1.csv\n"
     ]
    }
   ],
   "source": [
    "# initialize the class of CommunityGraphFactory\n",
    "cgf = CommunityGraphFactory()\n",
    "\n",
    "# Community detection for a list of graphs from local files\n",
    "\n",
    "graph_results = pd.DataFrame()\n",
    "node_results = []\n",
    "\n",
    "for graph_i in local_graph_list:\n",
    "    graph_name = graph_i['name']\n",
    "    num_comm = graph_i['num_comm']\n",
    "\n",
    "    assert graph_name in list(graph_file_dict.keys()), \\\n",
    "    \"Local graph name doesn't match graph names in 'graph_file_dict'\"\n",
    "\n",
    "    # load a graph from local files\n",
    "    graph = cgf.load_graph(graph_name, graph_file_dict)\n",
    "    \n",
    "    # run community detection using QBSolv\n",
    "    community_solver = CommunityDetectionSolver(graph_name, graph, num_comm, solver_limit=100, num_repeats=1, num_reads=1000, seed=1, alpha=5, mode='classical')\n",
    "    result, node_track = community_solver.run_community_detection(save=True)\n",
    "    \n",
    "    # append node-level results to the list of node_results\n",
    "    node_results.append(node_track)\n",
    "        \n",
    "    # convert result into dataframe\n",
    "    df_result_ = pd.DataFrame.from_dict(result)\n",
    "    graph_results = graph_results.append(df_result_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457fef8d",
   "metadata": {},
   "source": [
    "As in the synthetic graph case, we also log the solution results for real-world graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5ad37a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>graph_name</th>\n",
       "      <th>total_nodes</th>\n",
       "      <th>num_edge</th>\n",
       "      <th>num_comm</th>\n",
       "      <th>modu_qbsolv</th>\n",
       "      <th>num_comm_qbsolv</th>\n",
       "      <th>wall_time_s</th>\n",
       "      <th>solver_limit</th>\n",
       "      <th>num_repeats</th>\n",
       "      <th>num_reads</th>\n",
       "      <th>seed</th>\n",
       "      <th>alpha</th>\n",
       "      <th>mode</th>\n",
       "      <th>s3_folder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jazz</td>\n",
       "      <td>198</td>\n",
       "      <td>2742</td>\n",
       "      <td>2</td>\n",
       "      <td>0.305469</td>\n",
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       "      <td>Jazz</td>\n",
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       "      <td>3</td>\n",
       "      <td>1.41</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
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       "      <th>0</th>\n",
       "      <td>Jazz</td>\n",
       "      <td>198</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.440577</td>\n",
       "      <td>4</td>\n",
       "      <td>1.84</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dolphins</td>\n",
       "      <td>62</td>\n",
       "      <td>159</td>\n",
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       "      <td>4</td>\n",
       "      <td>0.38</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>classical</td>\n",
       "      <td>N/A</td>\n",
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      ],
      "text/plain": [
       "  graph_name  total_nodes  num_edge  num_comm  modu_qbsolv  num_comm_qbsolv  \\\n",
       "0       Jazz          198      2742         2     0.305469                2   \n",
       "0       Jazz          198      2742         3     0.444237                3   \n",
       "0       Jazz          198      2742         4     0.440577                4   \n",
       "0   Dolphins           62       159         5     0.526799                4   \n",
       "\n",
       "   wall_time_s  solver_limit  num_repeats  num_reads  seed  alpha       mode  \\\n",
       "0         0.49           100            1       1000     1      5  classical   \n",
       "0         1.41           100            1       1000     1      5  classical   \n",
       "0         1.84           100            1       1000     1      5  classical   \n",
       "0         0.38           100            1       1000     1      5  classical   \n",
       "\n",
       "  s3_folder  \n",
       "0       N/A  \n",
       "0       N/A  \n",
       "0       N/A  \n",
       "0       N/A  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# graph-level results for real-world graphs\n",
    "graph_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ced96193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'graphs': [('graph_Jazz_QBS_sl100_rp1_shot1000_seed1.gpickle', <networkx.classes.graph.Graph object at 0x7f2156450f50>)], 'responses': [('response_Jazz_QBS_sl100_rp1_shot1000_seed1.p', SampleSet(rec.array([([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0], -5.30546944, 2)],\n",
      "          dtype=[('sample', 'i1', (396,)), ('energy', '<f8'), ('num_occurrences', '<i8')]), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395], {}, 'BINARY'))], 'communities': [('modu_Jazz_QBS_sl100_rp1_shot1000_seed1.p', {'modularity': 0.30546943538686344, 'num_comm': 2, 'comm': [{0, 1, 2, 3, 4, 5, 11, 12, 13, 14, 16, 18, 20, 21, 22, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 71, 72, 76, 77, 78, 100, 120, 125, 126, 127, 128, 129, 130, 131, 132, 134, 135, 136, 140, 141, 142, 144, 145, 146, 147, 148, 149, 151, 152, 153, 155, 160, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 189, 191, 194, 195, 196}, {6, 7, 8, 9, 10, 15, 17, 19, 23, 24, 25, 26, 27, 47, 53, 54, 66, 73, 74, 75, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 133, 137, 138, 139, 143, 150, 154, 156, 157, 158, 159, 161, 162, 186, 187, 188, 190, 192, 193, 197}]})]}\n"
     ]
    }
   ],
   "source": [
    "# node-level results for one graph\n",
    "print(node_results[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a58298ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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