Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle
Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin Chang, Hongtai Cao, Zhen Jiang, Minghui Wu · IEEE Transactions on Knowledge and Data Engineering · 2021
SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying (e.g., single-pair, single-source and all-pair modes). In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common building blocks for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments on various real-world graphs show that to achieve the same accuracy, UISim runs faster than its respective state-of-the-art baselines in each mode, and scales well on larger graphs.