Channel-Attentive Graph Neural Networks

• URL: http://arxiv.org/abs/2503.00578v2
• Date: Wed, 05 Mar 2025 12:00:38 GMT
• Title: Channel-Attentive Graph Neural Networks
• Authors: Tuğrul Hasan Karabulut, İnci M. Baytaş,
• Abstract summary: Graph Neural Networks (GNNs) set the state-of-the-art in representation learning for graph-structured data.<n>Message-passing mechanism used in most models suffers from over-smoothing as a GNN's depth increases.<n>This study proposes an adaptive channel-wise message-passing approach to alleviate the over-smoothing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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