Node Classification on WikiCS (Accuracy and Ranking)
85.5AccuracyDCQ-GNN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DCQ-GNNRuns=10 independent runs2026.06 | 85.5 | 4.2 | |
| GCNIIRuns=10 independent runs2026.06 | 84.24 | 1.8 | |
| GATRuns=10 independent runs2026.06 | 84.23 | 7.4 | |
| BernNetRuns=10 independent runs2026.06 | 83.8 | 6.2 | |
| FAGCNRuns=10 independent runs2026.06 | 83.58 | 6.2 | |
| LHK-GNNRuns=10 independent runs2026.06 | 83.56 | 4.4 | |
| EvenNetRuns=10 independent runs2026.06 | 83.47 | 6 | |
| JK-NetRuns=10 independent runs2026.06 | 83.43 | 6.4 | |
| H2GCNRuns=10 independent runs2026.06 | 83.4 | 7.2 | |
| JacobiConvRuns=10 independent runs2026.06 | 83.1 | 7.2 | |
| GCNRuns=10 independent runs2026.06 | 82.02 | 9 | |
| GATModel=GAT, Training Source=Target dataset2026.04 | 79.27 | — | |
| GCNModel=GCN, Training Source=Target dataset2026.04 | 79.12 | — | |
| NodePFNModel=NodePFN, Training Source=Single pre-trained model2026.04 | 75.98 | — | |
| GraphAny (Products)Model=GraphAny, Training Source=Products2026.04 | 75.01 | — | |
| GraphAny (Arxiv)Model=GraphAny, Training Source=Arxiv2026.04 | 74.95 | — | |
| GraphAny (Cora)Model=GraphAny, Training Source=Cora2026.04 | 74.39 | — | |
| GraphAny (Wisconsin)Model=GraphAny, Training Source=Wisconsin2026.04 | 73.77 | — | |
| MLPModel=MLP, Training Source=Target dataset2026.04 | 72.72 | — |