Graph Classification
Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
( Image credit: Hierarchical Graph Pooling with Structure Learning )
Papers
Showing 1–10 of 927 papers
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | U2GNN (Unsupervised) | Accuracy | 95.67 | — | Unverified |
| 2 | MEWISPool | Accuracy | 84.33 | — | Unverified |
| 3 | DDGK | Accuracy | 83.14 | — | Unverified |
| 4 | Graph U-Nets | Accuracy | 82.43 | — | Unverified |
| 5 | DUGNN | Accuracy | 82.4 | — | Unverified |
| 6 | S2V (with 2 DiffPool) | Accuracy | 82.07 | — | Unverified |
| 7 | WKPI-kmeans | Accuracy | 82 | — | Unverified |
| 8 | hGANet | Accuracy | 81.71 | — | Unverified |
| 9 | HGP-SL | Accuracy | 80.96 | — | Unverified |
| 10 | SEAL-SAGE | Accuracy | 80.88 | — | Unverified |