Spectral Graph Methods for Community Detection in Complex Networks
Community detection is a key problem in complex-network analysis: densely connected groups may correspond to social circles, scientific fields, biological modules, or functional subsystems. This review considers how spectral graph methods translate a network into matrix form and then use eigenvalues and eigenvectors to uncover this structure. After introducing complex networks, graph matrices, community structure, and evaluation criteria, it develops the main ideas behind unnormalized and normalized graph Laplacians, the Fiedler vector, spectral embedding, normalized cut, and modularity-based eigenvector methods. Evidence from benchmark and real-world studies is then used to compare these classical techniques with regularized, non-backtracking, local, overlapping, neural-embedding, and randomized multi-layer extensions. Spectral methods remain competitive and mathematically interpretable. Their performance, however, depends on sparsity, degree heterogeneity, community overlap, network scale, and the choice of evaluation criteria. Future work is likely to combine sparse linear algebra and randomized eigensolvers with dynamic, multi-layer, and interpretable graph-learning models. By connecting core linear algebra with practical structure discovery, the review clarifies both the continuing value and the limits of the spectral perspective.