Graph Neural Networks for Community Detection in Graph Signal Analysis

• URL: http://arxiv.org/abs/2605.19733v1
• Date: Tue, 19 May 2026 12:07:51 GMT
• Title: Graph Neural Networks for Community Detection in Graph Signal Analysis
• Abstract summary: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing.<n>In recent years, Graph Neural Networks (GNNs) have emerged as effective tools for learning low-dimensional representations of graph-structured data.<n>This paper investigates the use of GNN-based community detection within a graph signal framework.
• Score: 42.46509410711165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Community detection is a central problem in graph analysis, with applications ranging from network science to graph signal processing. In recent years, Graph Neural Networks (GNNs) have emerged as effective tools for learning low-dimensional representations of graph-structured data and have shown strong performance in clustering tasks, particularly on large and high-dimensional graphs. This paper investigates the use of GNN-based community detection within a graph signal interpolation framework. After reviewing the main classes of GNN architectures for community detection according to a standard taxonomy, we integrate the resulting graph communities into a Partition of Unity Method (PUM) for interpolation with Graph Basis Functions (GBFs). In this approach, GNN-derived communities are used to construct local subdomains on which GBF interpolants are computed and subsequently combined into a global approximation. Numerical experiments on benchmark %graph datasets, including geometric and urban network examples demonstrate that the proposed combination of GNN-based clustering and GBF-PUM interpolation yields accurate signal reconstructions. The results indicate that deep learning-based community detection can provide effective graph partitions for localized interpolation schemes, supporting its use in scalable graph signal analysis.

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