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سند علمی

ECOviz: Comparative Visualization of Time-Evolving Network Summaries

Lisa Jin, Danai Koutra

چکیده

How can we visualize, interact with, and ‘learn’ important structures of time-evolving networks? Given domain-speci€c aŠributes, such as node membership of functional brain regions, how can we use this domain knowledge to discover coherent structures and track their evolution over time? In this demo paper, we introduce ECOviz (for Evolving COmparative network visualization), a system that enables pairwise comparison of temporal graph summaries based on variations in data source and preprocessing parameters. Our system further allows the user to perform structural and temporal analysis of a graph through ecient querying and visualization of its summarizing subgraphs. ECOviz performs the following tasks: (a) It generates a set of temporal structures for each graph of interest using a dynamic graph summarization algorithm o„ine; (b) It supports contrasting visual analysis of time-evolving network pairs by providing quantitative metrics on summary structure composition and temporal graph statistics; (c) It interactively visualizes the induced subgraph of each structure in a summary, at either a full time sequence or a time interval speci€ed by the user. In our demonstration, we invite the audience to use ECOviz to make comparisons between a variety of time-evolving functional human connectomes, and explore their salient temporal structures. ACM Reference format: Lisa Jin and Danai Koutra. 2017. ECOviz: Comparative Visualization of Time-Evolving Network Summaries. In KDD 2017 Workshop on Interactive Data Exploration and Analytics (IDEA’17), Halifax, Nova Scotia, Canada, August 14th, 2017, 8 pages.

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