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Graph pooling via Dual-view Multi-level Infomax by Qingqing Zhao; Han Zhang; Mengyao He; Wei Li; Chuanze Kang; Mingjing Han is a Computer Science article available to read on EtoBox.

What is Graph pooling via Dual-view Multi-level Infomax about?

Graph pooling is an essential component to improve the representation ability of graph neural networks. Existing pooling methods typically select a subset of nodes to generate an induced subgraph as the representation of the entire graph. However, they ignore the potential value of augmented views and cannot exploit the multi-level dependencies between representations. To address these problems, we propose Dual-view Multi-level Infomax Pooling (DMIPool), which can obtain and maximize the multi-level mutual information across dual-view representations. First, we generate a dual-view framework for graph pooling through data augmentation to encode hierarchical information and optimize representations. Next, we design a dynamic fusion mechanism to exploit both node features and graph topology information in dual views, and achieve comprehensive node-level and graph-level representations. Finally, to evaluate and further optimize the dual-view representations, we propose multi-level infomax, which maximizes mutual information of the representations across dual views at the graph–graph level, node–node level, and graph–node level. DMIPool can learn robust representations and preserve rep

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Author

Qingqing Zhao; Han Zhang; Mengyao He; Wei Li; Chuanze Kang; Mingjing Han

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)