Deep Graph Kernels
In this paper, we present Deep Graph Kernels (DGK), a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.
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Datasets
Introduced in the Paper:
IMDB-BINARY COLLAB IMDB-MULTI REDDIT-BINARY REDDIT-5K REDDIT-12KUsed in the Paper:
PROTEINS MUTAG ENZYMESResults from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Graph Classification | COLLAB | DGK | Accuracy | 73.09% | # 25 | |
| Graph Classification | D&D | DGK | Accuracy | 73.50% | # 41 | |
| Graph Classification | ENZYMES | DGK | Accuracy | 53.43% | # 30 | |
| Graph Classification | IMDb-B | DGK | Accuracy | 66.96% | # 38 | |
| Graph Classification | IMDb-M | DGK | Accuracy | 44.55% | # 32 | |
| Graph Classification | MUTAG | DGK | Accuracy | 87.44% | # 44 | |
| Graph Classification | PROTEINS | DGK | Accuracy | 75.68% | # 50 | |
| Graph Classification | RE-M12K | DGK | Accuracy | 32.22% | # 6 | |
| Graph Classification | RE-M5K | DGK | Accuracy | 41.27% | # 8 |
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