The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings... (read more)
PDFTASK | DATASET | MODEL | METRIC NAME | METRIC VALUE | GLOBAL RANK | BENCHMARK |
---|---|---|---|---|---|---|
Graph Classification | COLLAB | CapsGNN | Accuracy | 79.62% | # 12 | |
Graph Classification | D&D | CapsGNN | Accuracy | 75.38% | # 31 | |
Graph Classification | ENZYMES | CapsGNN | Accuracy | 54.67% | # 21 | |
Graph Classification | IMDb-B | CapsGNN | Accuracy | 73.10% | # 15 | |
Graph Classification | IMDb-M | CapsGNN | Accuracy | 50.27% | # 14 | |
Graph Classification | MUTAG | CapsGNN | Accuracy | 86.67% | # 35 | |
Graph Classification | NCI1 | CapsGNN | Accuracy | 78.35% | # 24 | |
Graph Classification | PROTEINS | CapsGNN | Accuracy | 76.28% | # 28 | |
Graph Classification | RE-M12K | CapsGNN | Accuracy | 46.62% | # 5 | |
Graph Classification | RE-M5K | CapsGNN | Accuracy | 52.88% | # 4 |
METHOD | TYPE | |
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🤖 No Methods Found | Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet |