Provable Filter for Real-world Graph Clustering

ArXi:2403.03666v2 Announce Type: replace Graph clustering, an important unsupervised problem, has been shown to be resistant to advances in Graph Neural Networks (GNNs). In addition, almost all clustering methods focus on homophilic graphs and ignore heterophily. This significantly limits their applicability in practice, since real-world graphs exhibit a structural disparity and cannot simply be classified as homophily and heterophily. Thus, a principled way to handle practical graphs is urgently needed. To fill this gap, we provide a novel solution with theoretical.