Networks comprise a useful structure for modeling and analyzing parts of a system (namely nodes) and their interaction (namely edges). Networks are used in biology and medicine (e.g., interactions of proteins or brain cells), computer sciences and telecommunication (e.g., emails, phone calls), social systems (e.g., friendship, proximity contacts), and many other disciplines. In several real-world situations, such networks evolve over time. Typical temporal patterns include, for example, periods with bursts of interactions and circadian rhythms in social networks. The visual analysis of these temporal networks comprehends an effective way to understand the network dynamics and identify patterns, anomalies, and other behaviors in the data. The problem, however, is a large amount of data in real-world networks, which may produce layouts with high visual clutter. In this seminar, I present three methods to improve layout readability, each manipulating a particular network dimension (namely node, edge, or time). These methods reveal meaningful patterns in the data, allowing users to gain insights that lead to fast and reliable decision-making.
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Jean R. Ponciano is currently a postdoctoral fellow in the School of Applied Mathematics at Fundação Getulio Vargas. He received the BSc (2012), MSc (2016), and Ph.D. (2020) degrees in computer science from the Federal University of Uberlandia, Brazil. During his Ph.D., he worked with Information Visualization, more specifically with the manipulation and visualization of complex, temporal, and streaming networks, optimizing the analysis of the structure of these networks and dynamic processes that take place on them.