Papers › Self-attention Dual Embedding for Graphs with Heterophily
Self-attention Dual Embedding for Graphs with Heterophily
Yurui Lai, Taiyan Zhang, Rui Fan
Graph Neural Networks (GNNs) have been highly successful for the node classification task. GNNs typically assume graphs are homophilic, i.e. neighboring nodes are likely to belong to the same class. However, a number of real-world graphs are heterophilic, and this leads to much lower classification accuracy using standard GNNs. In this work, we design a novel GNN which is effective for both heterophilic and homophilic graphs. Our work is based on three main observations. First, we show that node features and graph topology provide different amounts of informativeness in different graphs, and therefore they should be encoded independently and prioritized in an adaptive manner. Second, we show that allowing negative attention weights when propagating graph topology information improves accuracy. Finally, we show that asymmetric attention weights between nodes are helpful. We design a GNN which makes use of these observations through a novel self-attention mechanism. We evaluate our algorithm on real-world graphs containing thousands to millions of nodes and show that we achieve state-of-the-art results compared to existing GNNs. We also analyze the effectiveness of the main components of our design on different graphs.
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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 | Actor | SADE-GCN | Accuracy | 37.91 ± 0.97 | #12 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | SADE-GCN | Accuracy | 75.57±1.57 | #9 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | SADE-GCN | Accuracy | 86.21±5.59 | #12 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | SADE-GCN | Accuracy | 68.20±1.57 | #9 of 59 | Archive leaderboard | report |
| Node Classification | Texas | SADE-GCN | Accuracy | 86.49±5.12 | #20 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | SADE-GCN | Accuracy | 88.63±4.54 | #12 of 63 | 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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