The content introduces a novel approach to graph clustering that considers both homophilic and heterophilic graphs. By restructuring graphs and applying adaptive filters, the method demonstrates superior performance in practical scenarios. Experimental results validate the effectiveness of the proposed technique across various datasets.
Existing methods face challenges with homophily assumptions in graphs, leading to limited applicability in real-world scenarios.
The proposed Provable Filter for Graph Clustering (PFGC) method overcomes these limitations by capturing both low- and high-frequency information.
Through theoretical analysis and empirical experiments, PFGC showcases significant improvements in clustering accuracy compared to traditional methods.
Parameter analysis reveals the importance of integrating high-frequency information in heterophilic graphs while balancing low- and high-frequency components in homophilic graphs.
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arxiv.org
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