Fast Incremental PageRank on Dynamic Networks

Zexing Zhan, Ruimin Hu, Xiyue Gao, Nian Huai

Abstract: Real-world networks are very large and are constantly changing. Computing PageRank values for such dynamic networks is an important challenge in network science. In this paper, we propose an efficient Monte Carlo based algorithm for PageRank tracking on dynamic networks. A revisit probability model is also presented to provide theoretical support for our algorithm. For a graph with n nodes, the proposed algorithm maintains only nR random walk segments (R random walks starting from each node) in memory. The time cost to update PageRank scores for each graph modification is proportional to $$n/|\varvec{E}|$$ ( $$\varvec{E}$$ is the edge set). Experiments on 5 real-world networks indicate that our algorithm is 1.3–30 times faster than state-of-the-art algorithms and does not accumulate any errors.