Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A local updating algorithm for personalized PageRank via Chebyshev polynomials

A local updating algorithm for personalized PageRank via Chebyshev polynomials The personalized PageRank algorithm is one of the most versatile tools for the analysis of networks. In spite of its ubiquity, maintaining personalized PageRank vectors when the underlying network constantly evolves is still a challenging task. To address this limitation, this work proposes a novel distributed algorithm to locally update personalized PageRank vectors when the graph topology changes. The proposed algorithm is based on the use of Chebyshev polynomials and a novel update equation that encompasses a large family of PageRank-based methods. In particular, the algorithm has the following advantages: (i) it has faster convergence speed than state-of-the-art alternatives for local personalized PageRank updating; and (ii) it can update the solution of recent extensions of personalized PageRank that rely on complex dynamical processes for which no updating algorithms have been developed. Experiments in a real-world temporal network of an autonomous system validate the effectiveness of the proposed algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

A local updating algorithm for personalized PageRank via Chebyshev polynomials

Loading next page...
 
/lp/springer-journals/a-local-updating-algorithm-for-personalized-pagerank-via-chebyshev-3dyY7oZPLb
Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-022-00860-5
Publisher site
See Article on Publisher Site

Abstract

The personalized PageRank algorithm is one of the most versatile tools for the analysis of networks. In spite of its ubiquity, maintaining personalized PageRank vectors when the underlying network constantly evolves is still a challenging task. To address this limitation, this work proposes a novel distributed algorithm to locally update personalized PageRank vectors when the graph topology changes. The proposed algorithm is based on the use of Chebyshev polynomials and a novel update equation that encompasses a large family of PageRank-based methods. In particular, the algorithm has the following advantages: (i) it has faster convergence speed than state-of-the-art alternatives for local personalized PageRank updating; and (ii) it can update the solution of recent extensions of personalized PageRank that rely on complex dynamical processes for which no updating algorithms have been developed. Experiments in a real-world temporal network of an autonomous system validate the effectiveness of the proposed algorithm.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Dec 1, 2022

Keywords: PageRank; Updating algorithms; Local algorithms; Chebyshev polynomials; Graph signal processing; Semi-supervised learning

References