Francesco Tudisco

Associate Professor (Reader) in Machine Learning

School of Mathematics, The University of Edinburgh
The Maxwell Institute for Mathematical Sciences
School of Mathematics, Gran Sasso Science Institute JCMB, King’s Buildings, Edinburgh EH93FD UK
email: f dot tudisco at ed.ac.uk

A modularity based spectral method for simultaneous community and anti-community detection

Dario Fasino, Francesco Tudisco,
Linear Algebra and its Applications, 542 : 605--623 (2018)

Abstract

In a graph or complex network, communities and anti-communities are node sets whose modularity attains extremely large values, positive and negative, respectively. We consider the simultaneous detection of communities and anti-communities, by looking at spectral methods based on various matrix-based definitions of the modularity of a vertex set. Invariant subspaces associated to extreme eigenvalues of these matrices provide indications on the presence of both kinds of modular structure in the network. The localization of the relevant invariant subspaces can be estimated by looking at particular matrix angles based on Frobenius inner products.

Please cite this work as:

@article{fasino2017modularity,
  title={A modularity based spectral method for simultaneous community and anti-community detection},
  author={Fasino, Dario and Tudisco, Francesco},
  journal={Linear Algebra and its Applications},
  volume={542},
  pages={605--623},
  year={2018},
  publisher={Elsevier}
}

Links: doi arxiv

Keywords: Spectral clustering graph modularity community detection inflation product networks stochastic block model