Multi-order Graph Clustering with Adaptive Node-level Weight Learning

TL;DR

This paper introduces MOGC, a multi-order graph clustering model that integrates various higher-order motifs with adaptive node-level weight learning to improve clustering accuracy and address hypergraph fragmentation.

Contribution

It proposes a novel multi-order clustering approach with adaptive weight learning to effectively combine multiple motifs at the node level.

Findings

MOGC outperforms existing methods on seven real-world datasets.

Adaptive weight learning improves clustering accuracy.

Addresses hypergraph fragmentation issue effectively.

Abstract

Current graph clustering methods emphasize individual node and edge con nections, while ignoring higher-order organization at the level of motif. Re cently, higher-order graph clustering approaches have been designed by motif based hypergraphs. However, these approaches often suffer from hypergraph fragmentation issue seriously, which degrades the clustering performance greatly. Moreover, real-world graphs usually contain diverse motifs, with nodes participating in multiple motifs. A key challenge is how to achieve precise clustering results by integrating information from multiple motifs at the node level. In this paper, we propose a multi-order graph clustering model (MOGC) to integrate multiple higher-order structures and edge connections at node level. MOGC employs an adaptive weight learning mechanism to au tomatically adjust the contributions of different motifs for each node. This not only tackles hypergraph fragmentation issue but enhances clustering accuracy. MOGC is efficiently solved by an alternating minimization algo rithm. Experiments on seven real-world datasets illustrate the effectiveness of MOGC.