Interactive Time-Series of Measures for Exploring Dynamic Networks

Liwenhan Xie, James O'Donnel, Benjamin Bach, Jean-daniel Fekete

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present MeasureFlow, an interface to visually and interactively explore dynamic networks through time-series of network measures such as link number, graph density, or node activation. When networks contain many time steps, become large and more dense, or contain high frequencies of change, traditional visualizations that focus on network topology, such as animations or small multiples, fail to provide adequate overviews and thus fail to guide the analyst towards interesting time points and periods. MeasureFlow presents a complementary approach that relies on visualizing time-series of common network measures to provide a detailed yet comprehensive overview of when changes are happening and which network measures they involve. As dynamic networks undergo changes of varying rates and characteristics, network measures provide important hints on the pace and nature of their evolution and can guide an analysts in their exploration; based on a set of interactive and signal-processing methods, MeasureFlow allows an analyst to select and navigate periods of interest in the network. We demonstrate MeasureFlow through case studies with real-world data.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Advanced Visual Interfaces (AVI '20)
Number of pages9
ISBN (Print)9781450375351
Publication statusPublished - 28 Sep 2020
EventInternational Conference on Advanced Visual Interfaces 2020 - Ischia, Italy
Duration: 28 Sep 20203 Oct 2020


ConferenceInternational Conference on Advanced Visual Interfaces 2020
Internet address


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