A multi-hop Shapley-based framework for graph convolutional network node classification explanation

Yifan Zheng

doi:10.1016/j.asoc.2025.113615

2025-08-27

PRE

DOI：10.1016/j.asoc.2025.113615

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摘要

En 中文

• 提出一种计算图构建方法以整合多跳边。 • 引入一种采样策略以在不同规模下采样联盟。 • 通过关键边提高GCN对正确类别预测的置信度。

Keyword:

computational graph

multi-hop edges

sampling strategy

coalition sampling

GCN confidence

期刊

Applied Soft Computing

IF:

6.6

论文数:

1.4W

被引数:

4.8W

机构

School of Economics and Management

学者数:

769

论文数: 424

被引数: 2

computer

学者数:

157

论文数: 58

被引数: 0

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