Fast Computation of Eigenvector Centralities for Multilayer Networks with Nonnegative Tensor Train
Lobachevskii Journal of Mathematics, vol. 46, pp. 3804–3814, 2025
In this article, we introduce eigenvector centralities for higher-order multilayer networks and suggest to use nonnegative tensor train decomposition for fast computations of dominant eigenvectors of multi-homogeneous maps via power iterates. The analysis of the approximation error for using nonnegative tensor train …
Generalized Friendship Paradoxes in Network Science
SIAM Journal on Matrix Analysis and Applications, 2026
Generalized friendship paradoxes occur when, on average, our friends have more of some attribute than us. These paradoxes are relevant to many aspects of human interaction, notably in social science and epidemiology. Here, we derive new theoretical results concerning the inevitability of a paradox arising, using a …
Node and layer eigenvector centralities for multiplex networks
SIAM Journal on Applied Mathematics, vol. 78, pp. 853–876, 2018
Eigenvector-based centrality measures are among the most popular centrality measures in network science. The underlying idea is intuitive and the mathematical description is extremely simple in the framework of standard, mono-layer networks. Moreover, several efficient computational tools are available for their …