Where Graph Meets Heterogeneity: Multi-View Collaborative Graph Experts

The convergence of graph learning and multi-view learning has propelled the emergence of multi-view graph neural networks (MGNNs), offering strong capabilities to address complex real-world data characterized by heterogeneous yet interconnected information. While existing MGNNs exploit the potential of multi-view graphs, the inherent conflict persists between the two critical inductive biases of multi-view learning, consistency and complementarity. Consequently, the challenge of defining and resolving this tension in the new context of multi-view graphs remains largely underexplored. To bridge this gap, we propose Multi-view Collaborative Graph Experts (MvCGE), a novel framework grounded in the Mixture-of-Experts (MoE) paradigm. MvCGE establishes architectural consistency through shared parameters while preserving complementarity via layer-wise collaborative graph experts, which are dynamically activated by a graph-aware routing mechanism that adapts to the structural nuances of each view. This dual-level design is further reinforced by two novel components: a load equilibrium loss to prevent expert collapse and ensure balanced specialization, and a graph discrepancy loss based on distributional divergence to enhance inter-view complementarity. Extensive experiments on diverse datasets demonstrate MvCGE’s superiority.

Authors

• Zhihao Wu School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen
• Jinyu Cai Institute of Data Science, National University of Singapore, Singapore
• Yunhe Zhang Department of Computer and Information Science, SKL-IOTSC, University of Macau, China
• Jielong Lu
• Zhaoliang Chen Hong Kong Baptist University
• Shuman Zhuang Fuzhou University
• Haishuai Wang Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University, China

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