Unified and Incremental SimRank: Index-free Approximation with Scheduled Principle (Extended Abstract)
Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin Chen–Chuan Chang, Hongtai Cao, Zhen Jiang, Minghui Wu · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2022
SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying. 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, and scales well on larger graphs.