Link analysis using time series of web graphs

Link analysis is a key technology in contemporary web search engines. Most of the previous work on link analysis only used information from one snapshot of web graph. Since commercial search engines crawl the Web periodically, they will naturally obtain time series data of web graphs. The historical information contained in the series of web graphs can be used to improve the performance of link analysis. In this paper, we argue that page importance should be a dynamic quantity, and propose defining page importance as a function of both PageRank of the current web graph and accumulated historical page importance from previous web graphs. Specifically, a novel algorithm named TemporalRank is designed to compute the proposed page importance. We try to use a kinetic model to interpret this page importance and show that it can be regarded as the solution to an ordinary differential equation. Experiments on link analysis using web graph data in five snapshots show that the proposed algorithm can outperform PageRank in many measures, and can effectively filter out newly appeared link spam websites.