Papers › Clarify Confused Nodes via Separated Learning
Clarify Confused Nodes via Separated Learning
Jiajun Zhou, Shengbo Gong, Xuanze Chen, Chenxuan Xie, Shanqing Yu, Qi Xuan, Xiaoniu Yang
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, challenging the homophily assumption of traditional GNNs and hindering their performance. Most existing studies continue to design generic models with shared weights between heterophilous and homophilous nodes. Despite the incorporation of high-order messages or multi-channel architectures, these efforts often fall short. A minority of studies attempt to train different node groups separately but suffer from inappropriate separation metrics and low efficiency. In this paper, we first propose a new metric, termed Neighborhood Confusion (NC), to facilitate a more reliable separation of nodes. We observe that node groups with different levels of NC values exhibit certain differences in intra-group accuracy and visualized embeddings. These pave the way for Neighborhood Confusion-guided Graph Convolutional Network (NCGCN), in which nodes are grouped by their NC values and accept intra-group weight sharing and message passing. Extensive experiments on both homophilous and heterophilous benchmarks demonstrate that our framework can effectively separate nodes and yield significant performance improvement compared to the latest methods. The source code will be available in https://github.com/GISec-Team/NCGNN.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | AMZ Computers | NCGCN | Accuracy | 90.81 ± 0.46 | #1 of 5 | Archive leaderboard | report |
| Node Classification | AMZ Computers | NCSAGE | Accuracy | 90.43 ± 0.72 | #2 of 5 | Archive leaderboard | report |
| Node Classification | AMZ Photo | NCSAGE | Accuracy | 95.93 ± 0.36 | #1 of 14 | Archive leaderboard | report |
| Node Classification | AMZ Photo | NCGCN | Accuracy | 95.45 ± 0.45 | #3 of 14 | Archive leaderboard | report |
| Node Classification | Actor | NCSAGE | Accuracy | 43.89 ± 1.33 | #1 of 62 | Archive leaderboard | report |
| Node Classification | Actor | NCGCN | Accuracy | 43.16 ± 1.32 | #2 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon(60%/20%/20% random splits) | NCSAGE | Accuracy | 48.07 ± 3.47 | #1 of 2 | Archive leaderboard | report |
| Node Classification | Chameleon(60%/20%/20% random splits) | NCGCN | Accuracy | 46.84 ± 4.36 | #2 of 2 | Archive leaderboard | report |
| Node Classification | Coauthor CS | NCGCN | Accuracy | 96.64 ± 0.29 | #1 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor CS | NCSAGE | Accuracy | 96.48 ± 0.25 | #2 of 24 | Archive leaderboard | report |
| Node Classification | Coauthor Physics | NCSAGE | Accuracy | 98.69 ± 0.26 | #1 of 14 | Archive leaderboard | report |
| Node Classification | Coauthor Physics | NCGCN | Accuracy | 98.63 ± 0.24 | #2 of 14 | Archive leaderboard | report |
| Node Classification | Cora Full | NCSAGE | Accuracy | 72.58 ± 0.65% | #1 of 5 | Archive leaderboard | report |
| Node Classification | Cora Full-supervised | NCGCN | Accuracy | 73.42 ± 0.58% | #8 of 9 | Archive leaderboard | report |
| Node Classification | Penn94 | NCGCN | Accuracy | 84.74 ± 0.28 | #10 of 32 | Archive leaderboard | report |
| Node Classification | Penn94 | NCSAGE | Accuracy | 81.77 ± 0.71 | #17 of 32 | Archive leaderboard | report |
| Node Classification | Pubmed | NCGCN | Accuracy | 91.64 ± 0.53 | #1 of 70 | Archive leaderboard | report |
| Node Classification | Pubmed | NCSAGE | Accuracy | 91.55 ± 0.38 | #2 of 70 | Archive leaderboard | report |
| Node Classification | Squirrel(60%/20%/20% random splits) | NCSAGE | Accuracy | 54.42 ± 1.41 | #1 of 2 | Archive leaderboard | report |
| Node Classification | Squirrel(60%/20%/20% random splits) | NCGCN | Accuracy | 54.15 ± 1.47 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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