Research map / Graph learning / GNN architectures
Understanding and Enhancing Message Passing on Heterophilic Graphs via Compatibility Matrix
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Analyzes message passing on heterophilic graphs through the lens of a compatibility matrix and proposes an enhanced GNN design for heterophily (abstract only available).
Problem
Message passing in GNNs is assumed to fit homophily and seems unsuitable for heterophilic graphs, where connected nodes behave inconsistently.
Approach
Uses a compatibility matrix to understand when message passing remains effective on heterophilic graphs and uses this understanding to enhance message passing for heterophilic GNNs.
Closest papers by meaning
- Graph Neural Networks with Heterophily (AAAI 2021 · similarity 0.92)
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- Understanding Heterophily for Graph Neural Networks (ICML 2024 · similarity 0.89)
- HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations (NeurIPS 2025 · similarity 0.89)
- Integrating Co-Training with Edge Discrimination to Enhance Graph Neural Networks Under Heterophily (AAAI 2025 · similarity 0.89)
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