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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...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Weiyao Xu, Yumeng Sun, Yumeng MA, Yu Cheng, Jie Liu, Yuan Sheng, Yiwei Zhang, Junliang Du
Format: Artigo
Sprache:Inglês
Veröffentlicht: IEEE 2025-01-01
Schriftenreihe:IEEE Access
Schlagworte:
Frequent subgraph mining
graph neural network
graph classification
subgraph perception
joint optimization
Online-Zugang:https://ieeexplore.ieee.org/document/11141440/
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