Provable Filter for Real-world Graph Clustering: A Novel Approach with Theoretical Support

The author presents a novel solution for real-world graph clustering, addressing the limitations of existing methods by incorporating homophilic and heterophilic edges. By leveraging neighbor information, the proposed method outperforms state-of-the-art clustering techniques.