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Graph Neural Networks (GNNs) have shown excellent performance on graphs thatexhibit strong homophily with respect to the node labels i.e. connected nodeshave same labels. However, they perform poorly on heterophilic graphs. Recentapproaches have typically modified aggregation schemes, designed adaptive graphfilters, etc. to address this limitation. In spite of this, the performance onheterophilic graphs can still be poor. We propose a simple alternative methodthat exploits Truncated Singular Value Decomposition (TSVD) of topologicalstructure and node features. Our approach achieves up to ~30% improvement inperformance over state-of-the-art methods on heterophilic graphs. This work isan early investigation into methods that differ from aggregation basedapproaches. Our experimental results suggest that it might be important toexplore other alternatives to aggregation methods for heterophilic setting.

Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs