Papers › Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
Vijay Lingam, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam
Graph Neural Networks (GNNs) have shown excellent performance on graphs that exhibit strong homophily with respect to the node labels i.e. connected nodes have same labels. However, they perform poorly on heterophilic graphs. Recent approaches have typically modified aggregation schemes, designed adaptive graph filters, etc. to address this limitation. In spite of this, the performance on heterophilic graphs can still be poor. We propose a simple alternative method that exploits Truncated Singular Value Decomposition (TSVD) of topological structure and node features. Our approach achieves up to ~30% improvement in performance over state-of-the-art methods on heterophilic graphs. This work is an early investigation into methods that differ from aggregation based approaches. Our experimental results suggest that it might be important to explore other alternatives to aggregation methods for heterophilic setting.
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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 | HLP Concat | Accuracy | 34.59 ± 1.32 | #49 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | HLP Concat | Accuracy | 77.48±0.80 | #7 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | HLP Concat | Accuracy | 84.05±4.67 | #28 of 60 | Archive leaderboard | report |
| Node Classification | Crocodile | HLP Concat | Accuracy | 55.87±1.25 | #2 of 2 | Archive leaderboard | report |
| Node Classification | Squirrel | HLP Concat | Accuracy | 74.17±1.83 | #4 of 59 | Archive leaderboard | report |
| Node Classification | Texas | HLP Concat | Accuracy | 87.57 ± 5.44 | #14 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | HLP Concat | Accuracy | 86.67±4.22 | #35 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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