Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS

Abstract

We introduce a ranking model for temporal multi-dimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous order-preserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm.

Please cite this work as:

@inproceedings{arrigo2019multi,
  title={Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS},
  author={Arrigo, Francesca and Tudisco, Francesco},
  booktitle={Proceedings of the 2019 SIAM International Conference on Data Mining},
  pages={369--377},
  year={2019},
  organization={SIAM}
}

Keywords: network centrality multi-homogeneous maps multi-layer networks temporal networks Perron-Frobenius theory nonnegative tensors networks