An Inverse-Free Block-SOR Method With Randomly Sampling for Temporal Multiplex PageRank Problems

Ranking nodes in a multiplex network is one of the most pressing and challenging tasks in network analysis. Generalizing centrality measures to multiplex networks is an active area of research. The Multiplex PageRank model is an extension of Google's PageRank, which introduces a new centrality...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 35; no. 8; pp. 7736 - 7752
Main Authors Wu, Gang, Peng, Keke
Format Journal Article
LanguageEnglish
Published New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
centrality
Complex networks
Damping
inverse-free block-SOR method
Linear systems
Multiplex network
multiplex PageRank
Multiplexing
Network analysis
Nonhomogeneous media
Sampling
Search algorithms
Social networking (online)
Sparse matrices
temporal multiplex PageRank
temporal network
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Summary:Ranking nodes in a multiplex network is one of the most pressing and challenging tasks in network analysis. Generalizing centrality measures to multiplex networks is an active area of research. The Multiplex PageRank model is an extension of Google's PageRank, which introduces a new centrality measure to extend the usual PageRank to multiplex networks. In this work, we focus on the Multiplex PageRank problem. First, based on the special structure of the Multiplex PageRank problem, we propose an inverse-free block-SOR method. Second, with the help of randomly sampling, we propose a new strategy for estimating the optimal relaxation parameter. Specifically, the multiplex network is frequently updated in real world applications, and we have to deal with temporal Multiplex PageRank problems including the incremental and the decremental Multiplex PageRank problems. To the best of our knowledge, however, there are few efficient algorithms for solving these type of problems. To fill-in this gap, the third contribution of this work is to propose both incremental and decremental algorithms for solving the temporal Multiplex PageRank problems. Comprehensive numerical experiments are performed to illustrate the numerical behavior of the proposed algorithms, and show the effectiveness of our new strategies.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2022.3197920