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 | WKPI-kcenters | Accuracy | 87.3 | — | Unverified |
| 2 | WL-OA | Accuracy | 86.3 | — | Unverified |
| 3 | δ-2-LWL | Accuracy | 84.7 | — | Unverified |
| 4 | CIN++ | Accuracy | 84.5 | — | Unverified |
| 5 | PIN | Accuracy | 84 | — | Unverified |
| 6 | Spec-GN | Accuracy | 83.62 | — | Unverified |
| 7 | CAN | Accuracy | 83.6 | — | Unverified |
| 8 | Propagation kernels (pk) | Accuracy | 83.5 | — | Unverified |
| 9 | GIC | Accuracy | 82.86 | — | Unverified |
| 10 | PPGN | Accuracy | 82.23 | — | Unverified |