A class-aware representation refinement framework for graph classification
Graph Neural Networks (GNNs) are widely used for graph representation learning. Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the neglect of graph-level relationships, and the generalization issue. Each graph is treated separately in GNN message passing/gra...
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格式: | Article |
語言: | English |
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2024
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在線閱讀: | https://hdl.handle.net/10356/180546 |
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