An algebraic analysis of the graph modularity

Abstract

One of the most relevant tasks in network analysis is the detection of community structures, or clustering. Most popular techniques for community detection are based on the maximization of a quality function called modularity, which in turn is based upon particular quadratic forms associated to a real symmetric modularity matrix $M$, defined in terms of the adjacency matrix and a rank one null model matrix. That matrix could be posed inside the set of relevant matrices involved in graph theory, alongside adjacency, incidence and Laplacian matrices. This is the reason we propose a graph analysis based on the algebraic and spectral properties of such matrix. In particular, we propose a nodal domain theorem for the eigenvectors of $M$; we point out several relations occurring between graph’s communities and nonnegative eigenvalues of $M$; and we derive a Cheeger-type inequality for the graph optimal modularity.

Please cite this work as:

@article{fasino2014algebraic,
  title={An algebraic analysis of the graph modularity},
  author={Fasino, Dario and Tudisco, Francesco},
  journal={SIAM Journal on Matrix Analysis and Applications},
  volume={35},
  number={3},
  pages={997--1018},
  year={2014},
  publisher={SIAM}
}

Keywords: Graph partitioning Community detection Nodal domains graph modularity Spectral clustering networks cheeger inequality