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[example] add GGCM
Description
We add the implementation of GGCM method from the paper From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited. GGCM is a very practical unsupervised method, for the advantage of preserving graph structure without the need of learning any parameters.
Table 1: Node classification accuracy.
| Model | CiteSeer | Cora | PubMed |
|---|---|---|---|
| SGC (DGL) | 0.696 | 0.806 | 0.773 |
| GGCM (DGL) | 0.741 | 0.835 | 0.807 |
For comparison, we use DGL's build-in datasets and codes. As shown above, GGCM outperforms another classical "no-learning" method SGC by a large margin.
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.
- [ ] The PR title starts with [$CATEGORY] (such as [NN], [Model], [Doc], [Feature]])
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- [ ] The PR is complete and small, read the Google eng practice (CL equals to PR) to understand more about small PR. In DGL, we consider PRs with less than 200 lines of core code change are small (example, test and documentation could be exempted).
- [ ] All changes have test coverage
- [ ] Code is well-documented
- [ ] To the best of my knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
- [ ] Related issue is referred in this PR
- [ ] If the PR is for a new model/paper, I've updated the example index here.
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