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ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
[article]
2020
arXiv
pre-print
Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily
arXiv:1911.07979v3
fatcat:5zrtrymwxrdkljwq72yimo7ijm