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README.md

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

This repository contains a TensorFlow implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" by Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh (accepted as ORAL presentation in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2019).

Paper link: https://arxiv.org/pdf/1905.07953.pdf

Requirements

1) Download metis-5.1.0.tar.gz from http://glaros.dtc.umn.edu/gkhome/metis/metis/download and unpack it
2) cd metis-5.1.0
3) make config shared=1 prefix=~/.local/
4) make install
5) export METIS_DLL=~/.local/lib/libmetis.so
  • install required Python packages
 pip install -r requirements.txt

quick test to see whether you install metis correctly:

>>> import networkx as nx
>>> import metis
>>> G = metis.example_networkx()
>>> (edgecuts, parts) = metis.part_graph(G, 3)

  • We follow GraphSAGE's input format and its code for pre-processing the data.

  • This repository includes scripts for reproducing our experimental results on PPI and Reddit. Both datasets can be downloaded from this website.

Run Experiments.

  • After metis and networkx are set up, and datasets are ready, we can try the scripts.

  • We assume data files are stored under './data/{data-name}/' directory.

    For example, the path of PPI data files should be: data/ppi/ppi-{G.json, feats.npy, class_map.json, id_map.json}

  • For PPI data, you may run the following scripts to reproduce results in our paper

./run_ppi.sh

For reference, with a V100 GPU, running time per epoch on PPI is about 1 second.

The test F1 score will be around 0.9935 depending on different initialization.
  • For reddit data (need change the data_prefix path in .sh to point to the data):
./run_reddit.sh

Below shows a table of state-of-the-art performance from recent papers.

PPI Reddit
FastGCN (code) N/A 93.7
GraphSAGE (code) 61.2 95.4
VR-GCN (code) 97.8 96.3
GAT (code) 97.3 N/A
GaAN 98.71 96.36
GeniePath 98.5 N/A
Cluster-GCN 99.36 96.60

If you use any of the materials, please cite the following paper.

@inproceedings{clustergcn,
  title = {Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks},
  author = { Wei-Lin Chiang and Xuanqing Liu and Si Si and Yang Li and Samy Bengio and Cho-Jui Hsieh},
  booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year = {2019},
  url = {https://arxiv.org/pdf/1905.07953.pdf},
}