2 citations · 3 across the 5 of their papers we have counts for
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cs.LG2024★ 1 cited
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu, Bang Liu, Keith G. Mills +2
Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness. Subgraph-level explanations are gaining att…
cs.LG2024★ 2 cited
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu, Keith G. Mills, Jiao He +2
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training…
cs.LG2023
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
Boyu Chen, Hanxuan Chen, Jiao He +2
We present a simple yet novel parameterized form of linear mapping to achieves remarkable network compression performance: a pseudo SVD called Ternary SVD (TSVD). Unlike vanilla SV…