Random matrix improved community detection in heterogeneous networks

Hafiz Tiomoko Ali (1) , Romain Couillet (2)

1 L2S - Laboratoire des signaux et systèmes
2 NXP Semiconductors

—This article proposes a new spectral method for community detection in large dense networks following the degree-corrected stochastic block model. We theoretically support and analyze an approach based on a novel " α-regularization " of the modularity matrix. We provide a consistent estimator for the choice of α inducing the most favorable community detection in worst case scenarios. We further prove that spectral clustering ought to be performed on a 1 − α regularization of the dominant eigenvectors (rather than on the eigenvectors themselves) to compensate for biases due to degree heterogeneity. Although focused on dense graph models, our clustering method is shown to be very promising on real world networks with competitive performances versus state-of-the-art spectral techniques developed for sparse homogeneous networks.

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Statistiques [math.ST]; Réseaux sociaux et d'information [cs.SI]

Hafiz Tiomoko Ali, Romain Couillet. Random matrix improved community detection in heterogeneous networks. 50th Asilomar Conference on Signals, Systems and Computers, Nov 2016, Pacific Grove, United States. ⟨10.1109/acssc.2016.7869603⟩. ⟨hal-01812036⟩