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 | 95.62 | — | Unverified |
| 2 | TFGW ADJ (L=2) | Accuracy | 84.3 | — | Unverified |
| 3 | DUGNN | Accuracy | 84.2 | — | Unverified |
| 4 | G_DenseNet | Accuracy | 83.16 | — | Unverified |
| 5 | GFN | Accuracy | 81.5 | — | Unverified |
| 6 | PPGN | Accuracy | 81.38 | — | Unverified |
| 7 | GFN-light | Accuracy | 81.34 | — | Unverified |
| 8 | FactorGCN | Accuracy | 81.2 | — | Unverified |
| 9 | GMT | Accuracy | 80.74 | — | Unverified |
| 10 | sGIN | Accuracy | 80.71 | — | Unverified |