Multi-dimensional, multilayer, nonlinear and dynamic HITS

Arrigo, Francesca and Tudisco, Francesco (2019) Multi-dimensional, multilayer, nonlinear and dynamic HITS. In: SIAM International Conference on Data Mining, SDM 2019. SIAM, Philadelphia, PA, pp. 369-377. ISBN 9781611975673

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    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.