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 | DSGCN-allfeat | Accuracy | 78.39 | — | Unverified |
| 2 | TFGW SP (L=2) | Accuracy | 75.1 | — | Unverified |
| 3 | Norm-GN | Accuracy | 73.33 | — | Unverified |
| 4 | GDL-g (SP) | Accuracy | 71.47 | — | Unverified |
| 5 | FGW sp | Accuracy | 71 | — | Unverified |
| 6 | GFN | Accuracy | 70.17 | — | Unverified |
| 7 | GIUNet | Accuracy | 70 | — | Unverified |
| 8 | GFN-light | Accuracy | 69.5 | — | Unverified |
| 9 | HGP-SL | Accuracy | 68.79 | — | Unverified |
| 10 | G_Inception | Accuracy | 67.5 | — | Unverified |