Papers › Graph Capsule Convolutional Neural Networks
Graph Capsule Convolutional Neural Networks
Saurabh Verma, Zhi-Li Zhang
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision. In this paper, we expose and tackle some of the basic weaknesses of a GCNN model with a capsule idea presented in \cite{hinton2011transforming} and propose our Graph Capsule Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve especially graph classification problem which current GCNN models find challenging. Through extensive experiments, we show that our proposed Graph Capsule Network can significantly outperforms both the existing state-of-art deep learning methods and graph kernels on graph classification benchmark datasets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Classification | D&D | GCAPS-CNN | Accuracy | 77.62% | #31 of 53 | Archive leaderboard | report |
| Graph Classification | IMDb-B | GCAPS-CNN | Accuracy | 71.69% | #42 of 51 | Archive leaderboard | report |
| Graph Classification | NCI1 | GCAPS-CNN | Accuracy | 82.72% | #29 of 69 | Archive leaderboard | report |
| Graph Classification | PROTEINS | GCAPS-CNN | Accuracy | 76.40% | #47 of 103 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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