Provable Filter for Real-world Graph Clustering

Understand

Graph clustering, an important unsupervised problem, has been shown to be more 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.