Disentangling Homophily and Heterophily in Multimodal Graph Clustering

TL;DR

This paper introduces DMGC, a novel framework for multimodal graph clustering that disentangles homophilic and heterophilic relationships, improving clustering performance on complex multimodal graphs.

Contribution

The paper proposes a new framework, DMGC, which decomposes multimodal graphs into homophily and heterophily views and employs dual-frequency fusion for enhanced clustering.

Findings

DMGC achieves state-of-the-art results on multiple datasets.

Disentangling graph views improves clustering accuracy.

The approach generalizes across diverse multimodal graph types.

Abstract

Multimodal graphs, which integrate unstructured heterogeneous data with structured interconnections, offer substantial real-world utility but remain insufficiently explored in unsupervised learning. In this work, we initiate the study of multimodal graph clustering, aiming to bridge this critical gap. Through empirical analysis, we observe that real-world multimodal graphs often exhibit hybrid neighborhood patterns, combining both homophilic and heterophilic relationships. To address this challenge, we propose a novel framework -- \textsc{Disentangled Multimodal Graph Clustering (DMGC)} -- which decomposes the original hybrid graph into two complementary views: (1) a homophily-enhanced graph that captures cross-modal class consistency, and (2) heterophily-aware graphs that preserve modality-specific inter-class distinctions. We introduce a \emph{Multimodal Dual-frequency Fusion} mechanism that jointly filters these disentangled graphs through a dual-pass strategy, enabling effective multimodal integration while mitigating category confusion. Our self-supervised alignment objectives further guide the learning process without requiring labels. Extensive experiments on both multimodal and multi-relational graph datasets demonstrate that DMGC achieves state-of-the-art performance, highlighting its effectiveness and generalizability across diverse settings. Our code is available at https://github.com/Uncnbb/DMGC.