Cluster attention uses off-the-shelf community detection to define attention scopes within graph clusters, augmenting MPNNs and Graph Transformers to achieve larger receptive fields with preserved structural inductive biases and improved performance on diverse graph datasets.
There are two widely used definitions of clustering coefficients (Boccaletti et al., 2014): the global clustering coefficient and the average local clustering coefficient
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Cluster Attention for Graph Machine Learning
Cluster attention uses off-the-shelf community detection to define attention scopes within graph clusters, augmenting MPNNs and Graph Transformers to achieve larger receptive fields with preserved structural inductive biases and improved performance on diverse graph datasets.