Where Graph Meets Multi-View Learning : Collarborative Graph Mixture of Experts
Zhihao Wu, Jinyu Cai, Yunhe Zhang, Jielong Lu, Zhaoliang Chen, Shuman Zhuang, Haishuai Wang
The convergence of multi-view learning and graph learning has propelled the emergence of multi-view graph neural networks (MvGNNs), offering unprecedented capabilities to address complex real-world data characterized by heterogeneous yet interconnected information. While existing MvGNNs exploit the potential of multi-view graphs, they often fail to harmonize the dual inductive biases critical to multi-view learning: consistency (inherent inter-view agreement) and complementarity (view-specific distinctiveness). To bridge this gap, we propose Multi-view Collaborative Graph Mixture of 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.