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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 in [12,10]. Authors suggested that randomwalk-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.
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Contributor : Julie Queiros Connect in order to contact the contributor
Submitted on : Friday, October 15, 2021 - 2:13:58 PM
Last modification on : Tuesday, November 9, 2021 - 3:01:09 PM


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  • HAL Id : hal-03369197, version 1


Célestin Coquidé, Julie Queiros, François Queyroi. PageRank computation for Higher-Order Networks. Complex Networks 2021, Nov 2021, Madrid, Spain. ⟨hal-03369197v1⟩



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