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Merge pull request #11 from wey-gu/algo_verification
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feat: algo verification and docs
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wey-gu authored Feb 28, 2023
2 parents 217fd2e + 63fc7cb commit def52f4
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -49,7 +49,7 @@ pip install ngdi
- Spark 2.4, 3.0(not yet tested)
- [NebulaGraph 3.4+](https://github.com/vesoft-inc/nebula)
- [NebulaGraph Spark Connector 3.4+](https://repo1.maven.org/maven2/com/vesoft/nebula-spark-connector/)
- [NebulaGraph Algorithm 3.4+](https://repo1.maven.org/maven2/com/vesoft/nebula-algorithm/)
- [NebulaGraph Algorithm 3.1+](https://repo1.maven.org/maven2/com/vesoft/nebula-algorithm/)

### NebulaGraph Engine Prerequisites
- [NebulaGraph 3.4+](https://github.com/vesoft-inc/nebula)
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81 changes: 56 additions & 25 deletions examples/spark_engine.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "f46fdd40",
"metadata": {},
"outputs": [],
"source": [
"# install ngdi\n",
"!pip install ngdi"
]
},
{
"cell_type": "markdown",
"id": "8d81af21",
"id": "5b4e4143",
"metadata": {},
"source": [
"## Spark Engine Examples\n",
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},
{
"cell_type": "markdown",
"id": "8d0a7751",
"id": "f17abcf8",
"metadata": {},
"source": [
"In this example, we are leveraging the Spark Engine of NebulaGraph DI Suite, with the Storage Scan mode.\n",
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},
{
"cell_type": "markdown",
"id": "d29ebd47",
"id": "3617de5f",
"metadata": {},
"source": [
"#### Step 2, run Pagerank Algorithm"
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{
"cell_type": "code",
"execution_count": 2,
"id": "adeeacbe",
"id": "90069aaf",
"metadata": {},
"outputs": [
{
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"only showing top 20 rows\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"23/02/28 12:13:38 WARN BlockManager: Block rdd_75_4 already exists on this machine; not re-adding it\n"
]
}
],
"source": [
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},
{
"cell_type": "markdown",
"id": "ef66c9e2",
"id": "66e70ca0",
"metadata": {},
"source": [
"#### Step 3, check results of the algorithm\n"
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"+---------+-------------------+\n",
"| _id| pagerank|\n",
"+---------+-------------------+\n",
"|player133|0.18601069183310506|\n",
"|player126|0.18601069183310506|\n",
"|player130| 1.2400712788873671|\n",
"|player108|0.18601069183310506|\n",
"|player102| 1.6602373739502538|\n",
"|player133|0.18601069183310504|\n",
"|player126|0.18601069183310504|\n",
"|player130| 1.240071278887367|\n",
"|player108|0.18601069183310504|\n",
"|player102| 1.6602373739502536|\n",
"+---------+-------------------+\n",
"only showing top 5 rows\n",
"\n"
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},
{
"cell_type": "markdown",
"id": "4b9cf5fe",
"id": "3eb228f8",
"metadata": {},
"source": [
"------------------\n",
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{
"cell_type": "code",
"execution_count": 4,
"id": "b6542994",
"id": "e44ac3e9",
"metadata": {},
"outputs": [
{
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},
{
"cell_type": "markdown",
"id": "d24e5903",
"id": "49becbdb",
"metadata": {},
"source": [
"#### Step 2, run Conncted Components Algorithm"
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{
"cell_type": "code",
"execution_count": 5,
"id": "fc5dcc2c",
"id": "cfbcda82",
"metadata": {},
"outputs": [
{
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"only showing top 20 rows\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"23/02/27 11:29:58 WARN BlockManager: Block rdd_308_3 already exists on this machine; not re-adding it\n"
]
}
],
"source": [
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},
{
"cell_type": "markdown",
"id": "88a13923",
"id": "38181d45",
"metadata": {},
"source": [
"#### Step 3, check results of the algorithm\n"
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{
"cell_type": "code",
"execution_count": 6,
"id": "843b9662",
"id": "bed14375",
"metadata": {},
"outputs": [
{
Expand All @@ -349,13 +360,33 @@
"cc_result.show(5)"
]
},
{
"cell_type": "markdown",
"id": "5bcb02e2",
"metadata": {},
"source": [
"## Other algorithm examples"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8d97f96",
"id": "ff5a866d",
"metadata": {},
"outputs": [],
"source": []
"source": [
"# lpa_result = df.algo.label_propagation()\n",
"# louvain_result = df.algo.louvain()\n",
"# k_core_result = df.algo.k_core()\n",
"# degree_statics_result = df.algo.degree_statics()\n",
"# betweenness_centrality_result = df.algo.betweenness_centrality()\n",
"# coefficient_centrality_result = df.algo.coefficient_centrality()\n",
"# bfs_result = df.algo.bfs()\n",
"# hanp_result = df.algo.hanp()\n",
"# jaccard_result = df.algo.jaccard()\n",
"# strong_connected_components_result = df.algo.strong_connected_components()\n",
"# triangle_count_result = df.algo.triangle_count()"
]
}
],
"metadata": {
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