Everything Evolves in Personalized PageRank

Abstract

Personalized PageRank, as a graphical model, has been proven as an effective solution in many applications such as web page search, recommendation, etc. However, in the real world, the setting of personalized PageRank is usually dynamic like the evolving World Wide Web. On the one hand, the outdated PageRank solution can be sub-optimal for ignoring the evolution pattern. On the other hand, solving the solution from the scratch at each timestamp causes costly computation complexity. Hence, in this paper, we aim to solve the Personalized PageRank effectively and efficiently in a fully dynamic setting, i.e., every component in the Personalized PageRank formula is dependent on time. To this end, we propose the EvePPR method that can track the exact personalized PageRank solution at each timestamp in the fully dynamic setting, and we theoretically and empirically prove the accuracy and time complexity of EvePPR. Moreover, we apply EvePPR to solve the dynamic knowledge graph alignment task, where a fully dynamic setting is necessary but complex. The experiments show that EvePPR outperforms the state-of-the-art baselines for similar nodes retrieval across graphs.