Fast Computation of Eigenvector Centralities for Multilayer Networks with Nonnegative Tensor Train

Elena M. Shcherbakova, Francesco Tudisco, Eugene E. Tyrtyshnikov,
Lobachevskii J. Mathematics, 46 : 3804--3814 (2025)

Abstract

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 instead of the original nonnegative tensor is presented.

Please cite this work as:

@article{shcherbakova2025fast,
  title={Fast Computation of Eigenvector Centralities for Multilayer Networks with Nonnegative Tensor Train},
  author={Shcherbakova, Elizaveta M. and Tudisco, Francesco and Tyrtyshnikov, Eugene E.},
  journal={Lobachevskii Journal of Mathematics},
  volume={46},
  pages={3804--3814},
  year={2025},
  publisher={Springer}
}

Links: doi

Keywords: networks multilayer networks eigenvector centrality tensor train nonnegative tensors multi-homogeneous maps power method