CASE: A comprehensive and interactive influence analysis system for social networks (Demo)
Publication Type
Conference Proceeding Article
Publication Date
5-2026
Abstract
The rapid growth of social networks has made social influence analysis a critical research area. Despite significant progress, existing social influence analysis systems face two crucial limitations: insufficient functionality and inadequate interactivity. This highlights the need for a comprehensive system capable of supporting diverse influence analysis tasks while enabling interactive, multi-dimensional exploration of influence dynamics. In this demonstration, we present Case, a comprehensive and interactive influence analysis system for social networks. Case integrates three fundamental influence analysis functionalities: influence maximization, influence minimization, and the most-influenced community search, offering a holistic approach for understanding influence dynamics. It features (i) flexible model configuration supporting six distinct combinations of influence propagation and probability models; (ii) advanced algorithmic frameworks for seed node set selection, blocking node identification, and efficient community search; and (iii) transparent and interactive analysis that allows users to understand diffusion mechanisms and explore alternative intervention strategies. We demonstrate that Case provides an extensive suite of interactive visualization and analysis tools, achieving superior performance in real-world scenarios.
Discipline
Databases and Information Systems | Social Media
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 42nd IEEE International Conference on Data Engineering (ICDE'26), Montreal, QC, Canada, May 4-8
First Page
4189
Last Page
4192
Identifier
10.1109/ICDE65706.2026.00323
Publisher
IEEE
City or Country
Pistacataway
Citation
CHANG, Xueqin; LIU, Chuanyu; LIU, Qing; ZHENG, Baihua; and GAO, Yunjun.
CASE: A comprehensive and interactive influence analysis system for social networks (Demo). (2026). Proceedings of the 42nd IEEE International Conference on Data Engineering (ICDE'26), Montreal, QC, Canada, May 4-8. 4189-4192.
Available at: https://ink.library.smu.edu.sg/sis_research/11260