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Graph convolution networks (GCNs) have been enormously successful in learningrepresentations over several graph-based machine learning tasks. Specific tolearning rich node representations, most of the methods have solely relied onthe homophily assumption and have shown limited performance on theheterophilous graphs. While several methods have been developed with newarchitectures to address heterophily, we argue that by learning graphrepresentations across two spaces i.e., topology and feature space GCNs canaddress heterophily. In this work, we experimentally demonstrate theperformance of the proposed GCN framework over semi-supervised nodeclassification task on both homophilous and heterophilous graph benchmarks bylearning and combining representations across the topological and the featurespaces.

Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces