![]() To date, we have demonstrated SVEN by generating visualizations of several benchmark movie datasets: Star Wars, Inception, The Matrix (see ). The linear program’s objective is to minimize the sum total distance between groups and an optimal solution is found quickly using an off the shelf solver. Effective use of whitespace is found by defining the previous ordering and straightening properties as inequality and equality constraints in a linear program. Determining which lines will be straightened (without changing the order of groups) is framed as a maximum weighted independent set problem and solved using a simple greedy algorithm . We employ Graphviz’s “dot” algorithm, a directed graph layout technique, to determine an ordering for all groups of storylines that has few crossings. These groups are represented as nodes in a directed acyclic graph whose edges represent the flow of nodes between communities in adjacent time windows. This data is transformed into “interaction sessions” by discretizing time into several windows and finding communities that partitions the nodes into densely connected groups for each time window, similar to . Edges in a contact sequence are assumed to represent instantaneous interactions and can repeat at different times. Input for SVEN can take the form of a contact sequence, which is a list of edges and associated time stamps. We refer to this framework as “Storyline Visualization of Events on a Network” (SVEN). Our contribution is a framework that divides the overall storyline drawing problem (including addressing the three aesthetic criteria mentioned above) into relatively simple sub-problems having well-known solutions. Past work has approached this problem by using evolutionary or quadratic optimization techniques, developing complex ad-hoc solutions , or not addressing all of the established aesthetic criteria . There are well established criteria for drawing aesthetically pleasing storylines, which are to minimize (1) line crossings, (2) line wiggles, and (3) white space . Storyline visualization techniques hold promise, though these techniques were not originally designed as general purpose solutions for dynamic graph visualization. Simple approaches using animation or small multiples experience challenges with change blindness and “preserving the user’s mental map” . Techniques for general purpose dynamic graph visualizations generally fall into one of two broad categories: animation or timeline based techniques . Understanding this change is important, but often challenging. The world is a dynamic place, so when we use graphs to help understand real world problems the structure of such graphs inevitably changes over time.
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