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 | CRaWl | Accuracy | 93.15 | — | Unverified |
| 2 | GAT-GC (f-Scaled) | Accuracy | 92.57 | — | Unverified |
| 3 | GIN-0 | Accuracy | 92.4 | — | Unverified |
| 4 | DiffPool | Accuracy | 92.1 | — | Unverified |
| 5 | WEGL | Accuracy | 92 | — | Unverified |
| 6 | 2-WL-GNN | Accuracy | 89.4 | — | Unverified |
| 7 | δ-2-LWL | Accuracy | 89 | — | Unverified |
| 8 | NDP | Accuracy | 84.3 | — | Unverified |
| 9 | GraphSAGE | Accuracy | 84.3 | — | Unverified |
| 10 | ApproxRepSet | Accuracy | 80.3 | — | Unverified |