MoGL: A mixture of heterogeneous experts for collaborative graph learning

Graph Neural Networks (GNNs) have demonstrated remarkable success, yet they often exhibit limitations in capturing the complex local heterogeneity inherent in real-world graphs. While the Mixture of Experts (MoE) paradigm offers a promising direction, existing models are frequently constrained by standalone gating mechanisms, which introduce an unfavorable trade-off between predictive accuracy and computational efficiency. To address these challenges, we introduce the Mixture of Heterogeneous Experts for Collaborative Graph Learning (MoGL), a novel framework built upon a synergistic dual-expert architecture. MoGL strategically combines a standard GNN, tasked with capturing global structural information, with a lightweight and purpose-built local expert, the Graph Kolmogorov-Arnold Isomorphism Network (GKAIN). Inspired by the principles of Kolmogorov-Arnold Networks (KANs), GKAIN is uniquely designed to excel at modeling intricate non-linear relationships within local graph neighborhoods. To orchestrate the collaboration between these experts, we propose an innovative confidence-based gating mechanism that dynamically assigns task weights by leveraging the representations learned by GKAIN. This is further enhanced by a collaborative training paradigm, which employs a unified loss function to facilitate synergistic knowledge transfer between the experts. Extensive experiments on a diverse range of benchmarks demonstrate that MoGL achieves state-of-the-art performance on both homogeneous and heterogeneous graphs, thereby validating the superiority of our proposed architecture. The source code is available at: https://github.com/Season111/MoGL.