PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering
DOI:
https://doi.org/10.1609/aaai.v38i12.29246Keywords:
ML: Graph-based Machine Learning, DMKM: Graph Mining, Social Network Analysis & Community, ML: Representation Learning, ML: Semi-Supervised LearningAbstract
Recently, many carefully designed graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Therefore, they are incapable of generalizing well across real-world graphs with different levels of homophily. This is attributed to their neglect of homophily in heterophilic graphs, and vice versa. In this paper, we propose a two-fold filtering mechanism to mine homophily in heterophilic graphs, and vice versa. In particular, we extend the graph heat equation to perform heterophilic aggregation of global information from a long distance. The resultant filter can be exactly approximated by the Possion-Charlier (PC) polynomials. To further exploit information at multiple orders, we introduce a powerful graph convolution PC-Conv and its instantiation PCNet for the node classification task. Compared to the state-of-the-art GNNs, PCNet shows competitive performance on well-known homophilic and heterophilic graphs. Our implementation is available at https://github.com/uestclbh/PC-Conv.Downloads
Published
2024-03-24
How to Cite
Li, B., Pan, E., & Kang, Z. (2024). PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13437-13445. https://doi.org/10.1609/aaai.v38i12.29246
Issue
Section
AAAI Technical Track on Machine Learning III