no code implementations • 21 May 2024 • Zhaoning Yu, Hongyang Gao
To address these gaps, we introduce an innovative \textbf{M}otif-b\textbf{A}sed \textbf{G}NN \textbf{E}xplainer (MAGE) that uses motifs as fundamental units for generating explanations.
1 code implementation • 24 Dec 2023 • Zhaoning Yu, Hongyang Gao
In this paper, we develop a data-driven motif extraction technique known as MotifPiece, which employs statistical measures to define motifs.
1 code implementation • 1 Feb 2022 • Zhaoning Yu, Hongyang Gao
We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue.
no code implementations • 1 Feb 2022 • Zhaoning Yu, Hongyang Gao
Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substructures, which are more important for graph data.