Frequent subgraph-based persistent homology for graph classification

• URL: http://arxiv.org/abs/2512.24917v2
• Date: Fri, 02 Jan 2026 12:53:32 GMT
• Title: Frequent subgraph-based persistent homology for graph classification
• Authors: Xinyang Chen, Amaël Broustet, Guanyuan Zeng, Cheng He, Guoting Chen,
• Abstract summary: We propose a novel graph filtration called Frequent Subgraph filtration (FSF)<n>FSF is derived from frequent subgraphs and produces stable and information-rich frequency-based persistent homology (FPH) features.<n>We introduce two approaches for graph classification: an FPH-based machine learning model (FPH-ML) and a hybrid framework that integrates FPH with graph neural networks (FPH-GNNs)
• Score: 7.282967595233251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. However, most PH methods on graphs rely on a limited set of filtrations, such as degree-based or weight-based filtrations, which overlook richer features like recurring information across the dataset and thus restrict expressive power. In this work, we propose a novel graph filtration called Frequent Subgraph Filtration (FSF), which is derived from frequent subgraphs and produces stable and information-rich frequency-based persistent homology (FPH) features. We study the theoretical properties of FSF and provide both proofs and experimental validation. Beyond persistent homology itself, we introduce two approaches for graph classification: an FPH-based machine learning model (FPH-ML) and a hybrid framework that integrates FPH with graph neural networks (FPH-GNNs) to enhance topology-aware graph representation learning. Our frameworks bridge frequent subgraph mining and topological data analysis, offering a new perspective on topology-aware feature extraction. Experimental results show that FPH-ML achieves competitive or superior accuracy compared with kernel-based and degree-based filtration methods. When integrated into graph neural networks, FPH yields relative performance gains ranging from 0.4 to 21 percent, with improvements of up to 8.2 percentage points over GCN and GIN backbones across benchmarks.

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