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NetworkNarratives: Data Tours for Visual Network Exploration and Analysis

Authors :
Li, Wenchao
Schöttler, Sarah
Scott-Brown, James
Wang, Yun
Chen, Siming
Qu, Huamin
Bach, Benjamin
Publication Year :
2023

Abstract

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.<br />Comment: 15 pages. Published as a conference paper at the ACM Conference on Human Factors in Computing Systems (CHI) 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2303.06456
Document Type :
Working Paper
Full Text :
https://doi.org/10.1145/3544548.3581452