Multi-dimensional, multilayer, nonlinear and dynamic HITS

Francesca Arrigo, Francesco Tudisco

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

7 Citations (Scopus)
3 Downloads (Pure)

Abstract

We introduce a ranking model for temporal multidimensional 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.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
Place of PublicationPhiladelphia, PA
Pages369-377
Number of pages9
ISBN (Electronic)9781611975673
DOIs
Publication statusPublished - 4 May 2019
EventSIAM International Conference on Data Mining 2019 - Calgary, Canada
Duration: 2 May 20194 May 2019

Conference

ConferenceSIAM International Conference on Data Mining 2019
Abbreviated titleSDM19
CountryCanada
CityCalgary
Period2/05/194/05/19

Keywords

  • perron eigenvector
  • HITS
  • ranking model

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  • Projects

    MAGNET: Models and algorithms for graph analysis

    Tudisco, F. & Higham, D.

    1/07/17 → …

    Project: Research Fellowship

    Cite this

    Arrigo, F., & Tudisco, F. (2019). Multi-dimensional, multilayer, nonlinear and dynamic HITS. In SIAM International Conference on Data Mining, SDM 2019 (pp. 369-377). https://doi.org/10.1137/1.9781611975673.42