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OGC and GGCM - PyTorch Implementation

This repository contains the original PyTorch implementation of OGC and GGCM for semi-supervised node classification [1].

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PyG Implementation:

DGL Implementation:

Original Codes

Running OGC:

python main_ogc.py --dataset cora 
python main_ogc.py --dataset citeseer 
python main_ogc.py --dataset pubmed

Running GGCM:

python main_ggcm.py --dataset cora
python main_ggcm.py --dataset citeseer
python main_ggcm.py --dataset pubmed

References

[1] Wang, Zheng, Hongming Ding, Li Pan, Jianhua Li, Zhiguo Gong, and Philip S. Yu. "From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited." arXiv preprint arXiv:2309.13599 (2023).

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ogc_ggcm's Issues

Why compare the supervised OGC with other semi-supervised models?

Hallo. I appreciate your effort to analyse GCN from an optimization perspective and try to design GCN from a new perspective.

However, I noticed that OGC is a supervised method (mentioned in the abstract) since it involves the label information Y to update U (see eq.6). Therefore, I think that it is unfair to compare this supervised method with other semi-supervised models that use only 20 labels per class for training in Table 1.

Furthermore, research [1] shows (in Table 1) that GCN with ground true labels can achieve 100% accuracy on cora, citeseer, and pubmed datasets. Hope this information can help you.

[1] Topology Optimization based Graph Convolutional Network

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