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A multi-hop Shapley-based framework for graph convolutional network node classification explanation
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DOI：10.1016/j.asoc.2025.113615.png)
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Abstract
En 中文
• Propose a computational graph construction method to integrate multi-hop edges. • Introduce a sampling strategy to sample coalitions at diverse scales. • Improve GCN confidence in predicting the correct class with key edges.
Keywords:
computational graph
multi-hop edges
sampling strategy
coalition sampling
GCN confidence
Journal
IF:
6.6
Papers:
1.4W
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4.8W
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Cited Papers
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
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