Abstract

Recent years have seen increased growth and publication of graph datasets and complex networks. While the scientific study of networks is now an established area of research, information retrieval and visualization systems that address the needs of scientists and practitioners without technical expertise in network science algorithms and systems remain few and far between. Compounding the problem is that many large-scale network visualization systems are not publicly available under open licenses. As networks get larger, there is a clear need for an easy-touse, customizable, and dynamic visualization platform that offers facilities like fast vertex search and real-time pan-and-zoom. This demonstration paper presents ISAC, a system that uses an efficient hierarchical paradigm for enabling vertex search and visualization, even in networks with hundreds of thousands of nodes. Through an evaluation, we show that it can also be used to visually find the most influential (or central) nodes in a large network that correlate positively with theoretical measures of centrality in the literature.

Date

July 14, 2025

Authors

Navapat Nananukul, Khanin Sisaengsuwanchai, Mayank Kejriwal

Journal

Advances in Social Networks Analysis and Mining: Proceedings of the 16th International Conference on Advances in Social Networks Analysis and Mining-ASONAM 2024 Volume 1

Pages

89

Publisher

Springer Nature