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- W4286749649 abstract "Convolutional layers in graph neural networks are a fundamental type of layer which output a representation or embedding of each graph vertex. The representation typically encodes information about the vertex in question and its neighbourhood. If one wishes to perform a graph centric task, such as graph classification, this set of vertex representations must be integrated or pooled to form a graph representation. In this article we propose a novel pooling method which maps a set of vertex representations to a function space representation. This method is distinct from existing pooling methods which perform a mapping to either a vector or sequence space. Experimental graph classification results demonstrate that the proposed method generally outperforms most baseline pooling methods and in some cases achieves best performance." @default.
- W4286749649 created "2022-07-23" @default.
- W4286749649 creator A5028979534 @default.
- W4286749649 date "2019-05-15" @default.
- W4286749649 modified "2023-09-25" @default.
- W4286749649 title "Function Space Pooling For Graph Convolutional Networks" @default.
- W4286749649 doi "https://doi.org/10.48550/arxiv.1905.06259" @default.
- W4286749649 hasPublicationYear "2019" @default.
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