NeuroCUT: A Neural Approach for Robust Graph Partitioning

This repository is an official implementation of the Paper : NeuroCUT: A Neural Approach for Robust Graph Partitioning, accepted in KDD'24.

Commands to run

1. Activate Environment or create from requirements.txt

source {environment name}/bin/activate

2. Various parameters are listed in makefile, run commmand

make parameter_1={value_1} parameter_2={value_2} ..

Example command has been provided in the run.sh file.

Reults will be formed in the results folder, and model will be saved in models folder.

Sample Dataset

A sample Cora graph is given in the data folder. The structure of input graph is as follows:

<Graph_name> //input to the model
    <test_set> 
        <1>...<n>
    <val_set> 
        <1>...<n>
    <train_set> 
        <1>...<n>
// Each folder in train/val/test set should have a graph.txt and graph_stats.txt. The number of required cuts can be modifited in the graph_stats file.

Also, to run on any new graph, you need to add the graph.txt and node_embedding.pt file in raw_data folder.