The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery & data mining , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
SCGNN uses granular-ball computing to partition nodes into groups, builds an anchor-based augmented graph, and fuses predictions with label-consistency supervision to improve semantic consistency in GNNs.
citing papers explorer
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Attention-based graph neural networks: a survey
The survey groups attention-based GNNs into three stages—graph recurrent attention networks, graph attention networks, and graph transformers—while reviewing architectures and future directions.
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SCGNN: Semantic Consistency enhanced Graph Neural Network Guided by Granular-ball Computing
SCGNN uses granular-ball computing to partition nodes into groups, builds an anchor-based augmented graph, and fuses predictions with label-consistency supervision to improve semantic consistency in GNNs.