Understanding Complex Behavior: From Dynamic Graph Visualization to Visual Game Analytics, Fabian Beck & Shivam Agarwal
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Humans as well as machines exhibit complex behavior, already when acting alone, but even more when they interact with each other. Events and connections that evolve dynamically are embedded in spatial or non-spatial environments. Such scenarios can be found across various domains: Social networks, human gaze, software systems, or play data from computer games involve as actors human participants, traditional algorithms, and artificial, intelligent agents. To understand the recorded behavior, these scenarios can all be mapped to similar data structures and visualized through related methods. In our talk, we discuss dynamic graph visualization as a method to analyze such scenarios. We focus on timeline-based methods, which provide a good overview of temporal developments. Since insights can be specifically gained through contrast, visual comparison is a cross-cutting challenge. Finally, game analytics serves as a use case to study complex behavior in a controlled environment. When analyzing artificial agents competing in games, insights can be gained on what behavior the agents learned and strategies they follow.