Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS
This work addresses ranking challenges in complex temporal and multilayer networks, which is incremental as it builds upon the HITS algorithm with extensions for broader applicability.
The authors tackled the problem of ranking nodes in temporal, multi-dimensional, and multilayer networks by extending the HITS algorithm with nonlinearity and a multi-homogeneous map, resulting in a model that guarantees existence and uniqueness of centrality vectors for any network without connectivity requirements, and they developed a globally convergent algorithm validated through numerical experiments on real-world networks.
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.