Skip to Main content Skip to Navigation
Conference papers

PageRank computation for Higher-Order Networks

Abstract : 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 by 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.
Document type :
Conference papers
Complete list of metadata
Contributor : François Queyroi Connect in order to contact the contributor
Submitted on : Wednesday, November 3, 2021 - 8:41:33 AM
Last modification on : Wednesday, January 19, 2022 - 3:48:24 PM


Files produced by the author(s)


  • HAL Id : hal-03369197, version 2


Célestin Coquidé, Julie Queiros, François Queyroi. PageRank computation for Higher-Order Networks. Complex Networks 2021, Nov 2021, Madrid, Spain. ⟨hal-03369197v2⟩



Les métriques sont temporairement indisponibles