Dynamic Network Plaid

Abstract

Network data that changes over time can be very useful for studying a wide range of important phenomena, from how social network connections change to epidemiology. However, it is challenging to analyze, especially if it has many actors, connections or if the covered timespan is large with rapidly changing links (e.g., months of changes with changes at second resolution). In these analyses one would often like to compare many periods of time to others, without having to look at the full timeline. To support this kind of analysis we designed and implemented a technique and system to visualize this dynamic data. The Dynamic Network Plaid (DNP) is designed for large displays and based on user-generated interactive timeslicing on the dynamic graph attributes and on linked provenance-preserving representations. We present the technique, interface and the design/evaluation with a group of public health researchers investigating non-suicidal self-harm picture sharing in Instagram. CCS CONCEPTS • Human-centered computing → Visualization toolkits; User interface programming; Touch screens;

DynTrix: A Hybrid Representation for Dynamic Graphs

“…In one case, one participant decided to compare the characters in the first 3 chapters with the ones present in the last one (60) to solve the task. Large time sequences navigation is still an open problem [BBDW17,LAN19].…”

“…Methods for visualizing dynamic graphs primarily consist of time‐to‐time (see, e.g., [BPF14, FKN*04,GBPD04,BW04, FWSL12]) and time‐to‐space (see, e.g., [SA06, BVB*11, LHS*15, LAN19, BtBC*21]) techniques, whether if time is represented by replicating multiple time snapshots of the graph arranged closely on the drawing area or by the use of animation. Several human‐centered experiments have been conducted to compare and investigate the effectiveness of different methods (see, e.g., [FHQ11,OJK17, OJK19, LAN21, FABM23]).…”

The State of the Art in Visualizing Dynamic Multivariate Networks

“…To support users in exploring the temporal patterns, they offer interactive features such as semantic zooming, hierarchical aggregation, and dynamic filters. Due to the trade‐offs between intuitively showing topology using node‐link diagrams and reducing clutter using matrices for SNA visualizations, several DMVN visualizations implemented both approaches and conducted comparative studies [LAN19,BTBC * 21,NWHL20].…”

Personal Augmented Reality for Information Visualization on Large Interactive Displays

“…Data analysis benefits from touch input due to a more direct interaction with the data [20,38,43,49,51,68], although some use cases profit from more distant interaction [49][50][51]. The increased screen estate of the displays can be used for showing large amounts of data, such as node-link diagrams [50,68], or to incorporate multiple views of data [51,55]. Due to their size, large displays also facilitate collaborative data analysis and multiple analysts working in parallel [42,50,51,68,82].…”