[Example] add ogc method #6437
[Example] add ogc method #6437
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jermainewang
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Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>
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@BarclayII can you double check the implementation? |
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The math looks simple and I don't have any problems. My concerns are namely in code styles which is uncommon to PyTorch practices. Normally in PyTorch we write training code like this:
class OGC(nn.Module):
def __init__(self, ...):
# model parameter definition...
def forward(self, ...):
# Given the input, how to compute the output (i.e. according to Eq 6 in your paper)
...
model = OGC(...)
def train(model, ...):
optimizer = torch.optim.SGD(...)
for _ in range(epochs):
# compute loss...
# call optimizer...
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Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>
Description
An implementation of OGC method from the paper From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited.
OGC is a simple but very powerful graph convolution method, with only one trainable layer. Our initial proposal of the model mainly contains two files: utils.py which defines some basic functions, and main.py which shows the training progress.
Table 1: Node classification accuracy (%).
Table 2: Running time (s).
We use three DGL's built_in datasets (Citeseer, Cora and Pubmed) with their default train/val/test settings. As shown above, OGC has a substantial improvement, with 100-200X high efficiency compared to SOTA deep GNNs.
We are very happy to further improve the code for this great project, and will continue to contribute actively!
Author: Sinuo Xu (undergraduate, @sjtu) advised by Dr. Zheng Wang@SJTU.
Checklist
Please feel free to remove inapplicable items for your PR.