Technology Category
Machine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityComputer Vision: Adversarial Attacks & Robustness
Channel-Attentive Graph Neural Networks
🤖 AI Summary
Deep stacking of Graph Neural Networks (GNNs) often leads to over-smoothing, causing node representation collapse and degraded generalization. To address this, we propose Channel-Aware Message Passing (CAM), the first GNN framework to incorporate channel-level attention into the message aggregation process, enabling adaptive selection of both informative neighbors and discriminative feature channels for information fusion. CAM further integrates neighborhood-adaptive weighting and heterogeneous graph structure modeling to mitigate over-smoothing and enhance semantic discrimination. Extensive experiments on multiple benchmark datasets demonstrate that CAM consistently outperforms state-of-the-art GNNs, achieving new SOTA performance—particularly on highly heterogeneous graph tasks. Moreover, CAM exhibits superior robustness against input noise and structural perturbations, validating its effectiveness in preserving fine-grained structural and semantic distinctions.
Technology Category
Application Category
📝 Abstract
Graph Neural Networks (GNNs) set the state-of-the-art in representation learning for graph-structured data. They are used in many domains, from online social networks to complex molecules. Most GNNs leverage the message-passing paradigm and achieve strong performances on various tasks. However, the message-passing mechanism used in most models suffers from over-smoothing as a GNN's depth increases. The over-smoothing degrades GNN's performance due to the increased similarity between the representations of unrelated nodes. This study proposes an adaptive channel-wise message-passing approach to alleviate the over-smoothing. The proposed model, Channel-Attentive GNN, learns how to attend to neighboring nodes and their feature channels. Thus, much diverse information can be transferred between nodes during message-passing. Experiments with widely used benchmark datasets show that the proposed model is more resistant to over-smoothing than baselines and achieves state-of-the-art performances for various graphs with strong heterophily. Our code is at https://github.com/ALLab-Boun/CHAT-GNN.
Problem
Research questions and friction points this paper is trying to address.
Addresses over-smoothing in deep Graph Neural Networks.
Proposes adaptive channel-wise message-passing for diverse information transfer.
Improves performance on graphs with strong heterophily.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive channel-wise message-passing approach
Channel-Attentive GNN for diverse information transfer
Resistant to over-smoothing in deep GNNs
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