graphRAIN

This repository holds the script for graphRAIN algorithm - algorithm that creates explainable node embeddings of a graph.

Introduction

GraphRAIN stands for Graph Relational Attribute Integrated Node-embedding

This generates embedding vectors for nodes in a graph by incorporating the attributes hanging from the relationship between the nodes. Since the algorithm normalizes the relational attributes, this works for both heterogeneous and homogeneous graphs.

How to use

Following snippet shows how to use this package:

from embedding import RAIN

NCORES = <number of cores for parallel processing>
NBATCH = <in how many batches should the final embedding matrix to be saved>

metadata_dict = dict(
    edge_path = /path/to/edges.tsv,
    edge_metadata_path = /path/to/edge_metadata.tsv,
    node_metadata_path = /path/to/node_metadata.tsv,
    embedding_save_path = /path/to/save/output/files,
    graph_path = embedding_save_path/name_of_graph.joblib,    
)

embedding_nodeId_list = List of unique nodeIds of graph whose embedding vectors need to be computed

rain = RAIN()
rain.batch_embedding(
        metadata_dict = metadata_dict,
        embedding_nodeId_list = embedding_nodeId_list,
        ncores = NCORES,
        nbatch = NBATCH
    )

Refer to run_rain.py for a full example which you can run in your machine.

Sample data provided

Sample data provided is a network of disease and symptom nodes. This is a subnetwork taken from a bigger network called SPOKE. The objective of providing this sample data is to let user know about the format of the input files that one should provide to run RAIN algorithm.