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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

In the experiment section of the paper, we show how to generate Amazon2M dataset. There is an external implementation for generating Amazon2M data following the same procedure in the paper (code and data).

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},
}