Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs

@article{DallAmico2019OptimizedDL,
  title={Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs},
  author={Lorenzo Dall'Amico and Romain Couillet and Nicolas Tremblay},
  journal={CoRR},
  year={2019},
  volume={abs/1901.09715}
}
Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. In this article we study spectral clustering based on the deformed Laplacian matrix D − rA, for sparse heterogeneous graphs (following a two-class degreecorrected stochastic block model). For a specific value r = ζ, we show that, unlike competing methods such as the Bethe Hessian or nonbacktracking operator approaches, clustering is insensitive to the graph heterogeneity… CONTINUE READING

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