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Found 3 papers, 2 papers with code
MotifPiece: A Data-Driven Approach for Effective Motif Extraction and Molecular Representation Learning
In this paper, we develop a data-driven motif extraction technique known as MotifPiece, which employs statistical measures to define motifs.
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks
We propose a novel molecular graph representation learning method by constructing a heterogeneous motif graph to address this issue.
MotifExplainer: a Motif-based Graph Neural Network Explainer
Most existing GNN explanation methods identify the most important edges or nodes but fail to consider substructures, which are more important for graph data.
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