Graph Neural Diffusion via Generalized Opinion Dynamics

• URL: http://arxiv.org/abs/2508.11249v1
• Date: Fri, 15 Aug 2025 06:36:57 GMT
• Title: Graph Neural Diffusion via Generalized Opinion Dynamics
• Authors: Asela Hevapathige, Asiri Wijesinghe, Ahad N. Zehmakan,
• Abstract summary: We propose GODNF, which unifies multiple opinion dynamics models into a principled, trainable diffusion mechanism.<n>Our framework captures heterogeneous diffusion patterns and temporal dynamics via node-specific behavior modeling and dynamic neighborhood influence.<n>We provide a rigorous theoretical analysis demonstrating GODNF's ability to model diverse convergence configurations.
• Score: 8.691309696914882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical diffusion processes. However, existing methods suffer from three critical limitations: (1) they rely on homogeneous diffusion with static dynamics, limiting adaptability to diverse graph structures; (2) their depth is constrained by computational overhead and diminishing interpretability; and (3) theoretical understanding of their convergence behavior remains limited. To address these challenges, we propose GODNF, a Generalized Opinion Dynamics Neural Framework, which unifies multiple opinion dynamics models into a principled, trainable diffusion mechanism. Our framework captures heterogeneous diffusion patterns and temporal dynamics via node-specific behavior modeling and dynamic neighborhood influence, while ensuring efficient and interpretable message propagation even at deep layers. We provide a rigorous theoretical analysis demonstrating GODNF's ability to model diverse convergence configurations. Extensive empirical evaluations of node classification and influence estimation tasks confirm GODNF's superiority over state-of-the-art GNNs.

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