Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
5-2022
Abstract
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 stateof-the-art baselines, and scales well on larger graphs.
Keywords
SimRank approximation, unification, index-free, scheduled principle, scalability
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 38th International Conference on Data Engineering, Kuala Lumpur, Malaysia, 2022 May 9-12
First Page
1569
Last Page
1570
Identifier
10.1109/ICDE53745.2022.00161
Publisher
IEEE
City or Country
Kuala Lumpur, Malaysia
Citation
ZHU, Fanwei; FANG, Yuan; ZHANG, Kai; CHANG, Kevin Chen-Chuan; CAO, Hongtai; JIANG, Zhen; and WU, Minghui.
Unified and incremental SimRank: Index-free approximation with scheduled principle (extended abstract). (2022). Proceedings of the 38th International Conference on Data Engineering, Kuala Lumpur, Malaysia, 2022 May 9-12. 1569-1570.
Available at: https://ink.library.smu.edu.sg/sis_research/7497
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICDE53745.2022.00161