A local updating algorithm for personalized PageRank via Chebyshev polynomials

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