Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs

NeurIPS 2019 Lorenzo Dall'AmicoRomain CouilletNicolas Tremblay

Spectral clustering is one of the most popular, yet still incompletely understood, methods for community detection on graphs. This article studies spectral clustering based on the Bethe-Hessian matrix $H_r = (r^2-1)I_n + D-rA$ for sparse heterogeneous graphs (following the degree-corrected stochastic block model) in a two-class setting... (read more)

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