Cluster Attention for Graph Machine Learning

• URL: http://arxiv.org/abs/2604.07492v1
• Date: Wed, 08 Apr 2026 18:33:29 GMT
• Title: Cluster Attention for Graph Machine Learning
• Abstract summary: We propose an alternative approach to graph machine learning: cluster attention (CLATT)<n> CLATT provides large receptive fields while still having strong graph-structure-based inductive biases.<n>We show that augmenting Message Passing Neural Networks or Graph Transformers with CLATT significantly improves their performance on a wide range of graph datasets.
• Score: 15.901546570644264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers. To increase the receptive field, Graph Transformers with global attention have been proposed; however, global attention does not take into account the graph topology and thus lacks graph-structure-based inductive biases, which are typically very important for graph machine learning tasks. In this work, we propose an alternative approach: cluster attention (CLATT). We divide graph nodes into clusters with off-the-shelf graph community detection algorithms and let each node attend to all other nodes in each cluster. CLATT provides large receptive fields while still having strong graph-structure-based inductive biases. We show that augmenting Message Passing Neural Networks or Graph Transformers with CLATT significantly improves their performance on a wide range of graph datasets including datasets from the recently introduced GraphLand benchmark representing real-world applications of graph machine learning.

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