mlsanityData to 14 Oct 2026

Channel-Attentive Graph Neural Networks

arXiv 2503.00578 ↗First posted 1 Mar 2025cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 48 references that are in the map. First posted on arXiv 1 Mar 2025.

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39 of 48 references.

References · 39

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27 more
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