dg2pix: Pixel-Based Visual Analysis of Dynamic Graphs

Eren Cakmak, Dominik Jackle, Tobias Schreck, Daniel Keim
2020 2020 Visualization in Data Science (VDS)  
Figure 1 : dg2pix provides an overview of temporal and structural changes in dynamic graphs. The example presents a synthetic dynamic graph (200-time steps) using graph2vec [35] . The x-axis presents the temporal dimension, and the y-axis displays for each time step a graph embedding as a pixel-bar. The reoccurring states (A-C) have for each time step the same amount of nodes (2500) and edges (350000) with a different number of clusters. Each state (20-time steps) was generated with SBM [25]
more » ... h slight variations for the density of edges between clusters. The graphs (A-C) display a sample graph for each state. dg2pix enables us to explore and identify temporal changes, outlier graphs, and reoccurring graph structures at multiple temporal scales. ABSTRACT Presenting long sequences of dynamic graphs remains challenging due to the underlying large-scale and high-dimensional data. We propose dg2pix , a novel pixel-based visualization technique, to visually explore temporal and structural properties in long sequences of large-scale graphs. The approach consists of three main steps: (1) the multiscale modeling of the temporal dimension; (2) unsupervised graph embeddings to learn low-dimensional representations of the dynamic graph data; and (3) an interactive pixel-based visualization to simultaneously explore the evolving data at different temporal aggregation scales. dg2pix provides a scalable overview of a dynamic graph, supports the exploration of long sequences of high-dimensional graph data, and enables the identification and comparison of similar temporal states. We show the applicability of the technique to synthetic and real-world datasets, demonstrating that temporal patterns in dynamic graphs can be identified and interpreted over time. dg2pix contributes a suitable intermediate representation between node-link diagrams at the high detail end and matrix representations on the low detail end. © 2020 IEEE. This is the author's version of the article that has been published in the proceedings of IEEE Visualization conference. The final version of this record is available at: xx.xxxx/TVCG.201x.xxxxxxx/ and a pixel-based visualization. The graph embedding reduces the dynamic graph to a low-dimensional representation (50-300 dimensions) and learns the similarity between graphs to capture the evolving topology of the high-dimensional data. The compact visualization technique allows users to interactively adapt the temporal analysis scale and compare high-level as well as fine-grained structural changes. We demonstrate the usefulness of our approach through two use cases to show how dg2pix can be utilized to identify temporal changes and states in dynamic graphs. In summary, the contributions of this work are the following: (1) The novel dg2pix visualization technique, a time-scalable visual metaphor to reveal changes and similar temporal states in a dynamic graph; (2) an interpretation strategy of visual patterns that users can examine in dg2pix ; and (3) an interactive prototype that allows exploring dynamic graphs at multiple scales. RELATED WORK In the following, we briefly discuss related work from dynamic graph visualizations, the visual analysis of dimensionality reduction methods, and pixel-based visualization techniques.
doi:10.1109/vds51726.2020.00008 fatcat:yca23ponvbfcjnmuzqyyl7mdra