<ul><li>Hierarchical Graph Pooling (HGP) methods aim to address the limitations of conventional Graph Neural Networks (GNN) in terms of being inherently flat and lacking multiscale analysis.</li><li>However, most HGP methods fail to consider the global topology of the graph, focus primarily on feature learning, and align local and global features in a multiscale manner.</li><li>The proposed LGRPool method utilizes the expectation maximization framework in machine learning to align local and global aspects of message passing.</li><li>LGRPool introduces a regularizer to ensure that the global topological information is in line with the local message passing at different scales in HGP.</li></ul>

LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation

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