Cluster Attention for Graph Machine Learning
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...
| Authors | Oleg Platonov, Liudmila Prokhorenkova |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 7 August 2026 |
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