2021/09/07 by Célestin Coquidé, Coquidé, Célestin, Julie Queiros +3
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2109.03065
This article contains 11 pages, 5 figures, and is in submission process for the Complex Networks 2021 conference
arxiv created 2021/09/07 · arxiv updated 2021/09/08
Higher-order networks are efficient representations of sequential data. Unlike the classic first-order network approach, they capture indirect dependencies between items composing the input sequences by the use of memory-nodes. We focus in this study on the variable-order network model introduced in [Xu et al. (2016);Saebi et al. (2020)]. Authors suggested that random-walk-based mining tools can be directly applied to these networks. We discuss the case of the PageRank measure. We show the existence of a bias due to the distribution of the number of representations of the items. We propose an adaptation of the PageRank model in order to correct it. Application on real-world data shows important differences in the achieved rankings. \keywordsHigher-order Networks, Sequential data, Random walks, PageRank