*: Equal Contribution
This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the paper Simplifying Graph Convolutional Networks.
SGC removes the nonlinearities and collapes the weight matrices in Graph Convolutional Networks (GCNs) and is essentially a linear model.
For an illustration, 
SGC achieves competitive performance while saving much training time. For reference, on a GTX 1080 Ti,
| Dataset | Metric | Training Time |
|---|---|---|
| Cora | Acc: 81.0 % | 0.13s |
| Citeseer | Acc: 71.9 % | 0.14s |
| Pubmed | Acc: 78.9 % | 0.29s |
| F1: 94.9 % | 2.7s |
This home repo contains the implementation for citation networks (Cora, Citeseer, and Pubmed) and social network (Reddit). We have a work-in-progress branch ablation, containing additional codebase for our ablation studies.
If you find this repo useful, please cite:
@article{sgc,
title={Simplifying Graph Convolutional Networks},
author={Wu, Felix and Zhang, Tianyi and Souza Jr., Amauri Holanda and Fifty, Christopher and Yu, Tao and Weinberger, Kilian Q.},
journal={arXiv preprint arXiv:1902.07153},
year={2019}
}
Other reference implementations can be found in the follwing libraries. Note that in these examples, the hyperparameters are potentially different and the results would be different from the paper reported ones.
- Deep Graph Library: example.
- PyTorch Geometric: documentation and example.
- Spektral: example
Our implementation works with PyTorch>=1.0.0 Install other dependencies: $ pip install -r requirement.txt
We provide the citation network datasets under data/, which corresponds to the public data splits.
Due to space limit, please download reddit dataset from FastGCN and put reddit_adj.npz, reddit.npz under data/.
Citation Networks: We tune the only hyperparameter, weight decay, with hyperopt and put the resulting hyperparameter under SGC-tuning.
See tuning.py for more details on hyperparameter optimization.
$ python citation.py --dataset cora --tuned
$ python citation.py --dataset citeseer --tuned --epochs 150
$ python citation.py --dataset pubmed --tuned
Reddit:
$ python reddit.py --inductive --test
We collect the code base for downstream tasks under downstream. Currently, we
are releasing only SGC implementation for text classification. More downstream
tasks are coming soon.
This repo is modified from pygcn, and FastGCN.
We thank Deep Graph Library team for helping providing a reference implementation of SGC and benchmarking SGC in Deep Graph Library. We thank Matthias Fey, author of PyTorch Geometric, for his help on providing a reference implementation of SGC within PyTorch Geometric. We thank Daniele Grattarola, author of Spektral, for his help on providing a reference implementation of SGC within Spektral.