EXP-GNN: Towards expressive propagation function design for graph neural network-based knowledge graph completion

doi:10.1016/j.eswa.2025.129957

2025-10-14

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DOI：10.1016/j.eswa.2025.129957

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• GNNs的注入性原理适用于单关系和多关系图。 • 现有表达性GNN传播函数可以直接扩展到KGC任务中。 • 更具表达力的GNN必然会产生更具表达力的基于GNN的KGC模型。 • 注入性和强邻居特征提取能力本质上是一致的。

期刊

Expert Systems with Applications

IF:

7.5

论文数:

3.0W

被引数:

10.2W

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