Graph Neural Networks for Community Detection in Graph Signal Analysis

arXiv:2605.19733v1 Announce Type: cross 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 architect

Source: arXiv cs.LG — read the full report at the original publisher.

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