2020
Generating large scale-free networks with the Chung-Lu random graph model
Networks, 2020
Being able to produce synthetic networks by means of generative random graph models and scalable algorithms is a recurring tool-of-the-trade in network analysis, as it provides a well founded basis for the statistical analysis of various properties in real-world networks. In this paper, we illustrate how to generate …
2018
The expected adjacency and modularity matrices in the degree corrected stochastic block model
Special Matrices, vol. 6, pp. 110–121, 2018
We provide explicit expressions for the eigenvalues and eigenvectors of matrices that can be written as the Hadamard product of a block partitioned matrix with constant blocks and a rank one matrix. Such matrices arise as the expected adjacency or modularity matrices in certain random graph models that are widely used …
A modularity based spectral method for simultaneous community and anti-community detection
Linear Algebra and its Applications, vol. 542, pp. 605–623, 2018
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 …