Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Evolutionary Layout of Graph Transformation Sequences

Graph transformation is used in various different research areas and has been implemented in several tool environments. However, the layout of graph transformation sequences is often perceived as not optimal and remains to be a difficult task. This is partly due to the slightly different requirements for layouting graph transformation sequences compared to standard graph sequences. In this paper, we clearly define these special requirements and present a layout algorithm which fulfills them. This layout algorithm allows the user to keep track of changes during transformation steps by introducing a concept of node aging and protection of senior node positions in the layout. Furthermore, this layout algorithm introduces a concept of layout patterns. We extended the well-known spring embedder layout algorithm by these new concepts and implemented the new algorithm in AGG, an environment for Attributed Graph Grammars. The layout algorithm has been tested with various graph grammars. A brief outlook describes how this layout algorithm can also be used for different kinds of graph sequences, e.g. sequences of successively developing class diagrams

Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity

Uniform timeslicing of dynamic graphs has been used due to its convenience and uniformity across the time dimension. However, uniform timeslicing does not take the data set into account, which can generate cluttered timeslices with edge bursts and empty timeslices with few interactions. The graph mining filed has explored nonuniform timeslicing methods specifically designed to preserve graph features for mining tasks. In this paper, we propose a nonuniform timeslicing approach for dynamic graph visualization. Our goal is to create timeslices of equal visual complexity. To this end, we adapt histogram equalization to create timeslices with a similar number of events, balancing the visual complexity across timeslices and conveying more important details of timeslices with bursting edges. A case study has been conducted, in comparison with uniform timeslicing, to demonstrate the effectiveness of our approach.Comment: 5 pages, 4 figures, IEEE VIS short pape

Visualizing Evolving Trees

Evolving trees arise in many real-life scenarios from computer file systems and dynamic call graphs, to fake news propagation and disease spread. Most layout algorithms for static trees, however, do not work well in an evolving setting (e.g., they are not designed to be stable between time steps). Dynamic graph layout algorithms are better suited to this task, although they often introduce unnecessary edge crossings. With this in mind we propose two methods for visualizing evolving trees that guarantee no edge crossings, while optimizing (1) desired edge length realization, (2) layout compactness, and (3) stability. We evaluate the two new methods, along with four prior approaches (two static and two dynamic), on real-world datasets using quantitative metrics: stress, desired edge length realization, layout compactness, stability, and running time. The new methods are fully functional and available on github