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A diffusion-perception co-learning framework for deep graph neural networks
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DOI：10.1016/j.neunet.2026.109396.png)
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Abstract
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The advancement of deep graph neural networks is hindered by two fundamental challenges in message passing: over-smoothing and over-squashing. Existing graph representation learning methods typically address these problems separately, using homogeneous aggregation strategies that overlook the role-specific nodes in information interaction, making it challenging to preserve critical signals while reducing redundancy. To address these limitations, we propose a novel Diffusion-Perception Co-Learning framework, called DPCoL, which adaptively models the distinct roles of nodes to optimize message passing in deep graph neural networks. DPCoL defines a two-stage message-passing paradigm: (1) Diffusion, where each node dynamically modulates propagation intensity based on feature discriminability, and (2) Perception, where each node selectively identifies task-relevant information from aggregated messages. This co-learning mechanism prevents excessive feature mixing while preserving meaningful signals, ensuring effective long-range message passing without relying on prior assumptions. Unlike previous methods dependent on hyperparameter tuning or explicit smoothing controls, DPCoL employs a learnable diffusion controller and message perceptron, achieving self-adaptive interactions without additional computational overhead. Theoretical analysis validates the convergence properties and expressiveness of DP-CoL in addressing the aforementioned problems. Extensive experiments on 13 benchmark datasets, including homophilic, heterophilic, and long-range dependency graphs, demonstrate the robustness and superior performance of the model. DPCoL consistently achieves state-of-the-art results across most scenarios.
Keywords:
Deep graph neural networks
Over-smoothing
Over-squashing
Role-specific node adaptation
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