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 | GRIT | Accuracy (%) | 76.47 | — | Unverified |
| 2 | TIGT | Accuracy (%) | 73.96 | — | Unverified |
| 3 | ARGNP | Accuracy (%) | 73.9 | — | Unverified |
| 4 | DGN | Accuracy (%) | 72.84 | — | Unverified |
| 5 | GPS | Accuracy (%) | 72.3 | — | Unverified |
| 6 | PNA | Accuracy (%) | 70.47 | — | Unverified |
| 7 | EIGENFORMER | Accuracy (%) | 70.19 | — | Unverified |
| 8 | GatedGCN | Accuracy (%) | 69.37 | — | Unverified |
| 9 | EGT | Accuracy (%) | 68.7 | — | Unverified |
| 10 | GatedGCN | Accuracy (%) | 67.31 | — | Unverified |