Discrete Multi-View Feature Propagation Preserving Graph Clustering

Graph clustering is a fundamental and challenging learning task, which is conventionally approached by grouping similar vertices based on edge structure and feature similarity. In contrast to previous methods, in this paper, we investigate how multi-view feature propagation can influence cluster discovery in graph data. To this end, we present Discrete Multi-View Feature Propagation Preserving Graph Clustering (DMVFPPGC), a novel method that leverages multi-view feature propagation to enhance cluster identification in graph data. DMVFPPGC employs a unified objective function that utilizes graph topology and multi-view vertex features to determine vertex cluster membership, regularized by a module that supports key latent feature propagation. We derive an iterative algorithm to optimize this function, prove model convergence within a finite number of iterations, and analyze its computational complexity. Our experiments on various real-world graphs demonstrate the superior clustering performance of DMVFPPGC compared to well-established methods, manifesting its effectiveness across different scenarios.