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Residual convolutional graph neural network with subgraph attention pooling
2022
Tsinghua Science and Technology
The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational complexity. However, pooling shrinkage discards graph details, and existing pooling methods may lead to the loss of key classification features. In this work, we propose a residual convolutional graph neural network to tackle the problem of key classification features losing. Particularly, our contributions are threefold: (1) Different from existing methods,
doi:10.26599/tst.2021.9010058
fatcat:bz4cfxu2nvf4xcd7wn7sotuow4