Research Paper#Graph Classification, Persistent Homology, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 06:21
Frequent Subgraph-based Persistent Homology for Graph Classification
Published:Dec 31, 2025 15:21
•1 min read
•ArXiv
Analysis
This paper introduces a novel graph filtration method, Frequent Subgraph Filtration (FSF), to improve graph classification by leveraging persistent homology. It addresses the limitations of existing methods that rely on simpler filtrations by incorporating richer features from frequent subgraphs. The paper proposes two classification approaches: an FPH-based machine learning model and a hybrid framework integrating FPH with graph neural networks. The results demonstrate competitive or superior accuracy compared to existing methods, highlighting the potential of FSF for topology-aware feature extraction in graph analysis.
Key Takeaways
Reference
“The paper's key finding is the development of FSF and its successful application in graph classification, leading to improved performance compared to existing methods, especially when integrated with graph neural networks.”