# A Local Updating Algorithm for Personalized PageRank via Chebyshev Polynomials

arXiv: https://arxiv.org/abs/2110.02538 · HTML: https://mlsanity.com/paper/2110.02538/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2021.

## Built on by (most cited first, 1 of 1)

- [Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach](https://mlsanity.com/paper/2412.10789/) (2024)

## Abstract (arXiv)

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 PageRank updating; and (ii) it can update the solution of recent generalizations of PageRank 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.