Diffusion-based Graph-agnostic Clustering

Clustering over a graph seeks to partition the nodes therein into disjoint groups such that nodes within the same cluster are tightly-knit, while those across clusters are distant from each other. In practice, graphs are often attended with rich attributes, which are termed attributed graphs. By leveraging the complementary nature of graph topology and node attributes in such graphs, graph neural networks (GNNs) have obtained encouraging performance in graph clustering. However, existing GNN-based approaches strongly rely on the homophilic assumption of the input graph, and thus, largely fail on heterophilic graphs and others embodying numerous missing or noisy links, which are widely present in real life.