Subgraph-Aware Joint Modeling for Graph Classification via Efficient Frequent Pattern Mining and Fusion

This paper proposes a subgraph-aware classification framework that integrates efficient frequent subgraph mining with graph neural networks (GNNs) to address the limitations of existing GNNs in capturing explicit local structures. Specifically,we first introduce an improved mining strategy to extrac...

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Detalles Bibliográficos

Autores principales:	Weiyao Xu, Yumeng Sun, Yumeng MA, Yu Cheng, Jie Liu, Yuan Sheng, Yiwei Zhang, Junliang Du
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2025-01-01
Colección:	IEEE Access
Materias:	Frequent subgraph mining graph neural network graph classification subgraph perception joint optimization
Acceso en línea:	https://ieeexplore.ieee.org/document/11141440/
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