Related papers: Graph Pooling via Coarsened Graph Infomax

Graph Pooling via Coarsened Graph Infomax

URL: http://arxiv.org/abs/2105.01275v1
Date: Tue, 4 May 2021 03:50:21 GMT
Title: Graph Pooling via Coarsened Graph Infomax
Authors: Yunsheng Pang, Yunxiang Zhao, Dongsheng Li
Abstract summary: We propose Coarsened GraphPool Infomaxing (CGI) to maximize the mutual information between the input and the coarsened graph of each pooling layer. To achieve mutual information neural, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples.
Score: 9.045707667111873
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling. To address the problems of existing graph pooling methods, we propose Coarsened Graph Infomax Pooling (CGIPool) that maximizes the mutual information between the input and the coarsened graph of each pooling layer to preserve graph-level dependencies. To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples. Extensive experimental results on seven datasets illustrate the superiority of CGIPool comparing to the state-of-the-art methods.

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