Visual Adjacency Lists for Dynamic Graphs

Abstract

We present a visual representation for dynamic, weighted graphs based on the concept of adjacency lists. Two orthogonal axes are used: one for all nodes of the displayed graph, the other for the corresponding links. Colors and labels are employed to identify the nodes. The usage of color allows us to scale the visualization to single pixel level for large graphs. In contrast to other techniques, we employ an asymmetric mapping that results in an aligned and compact representation of links. Our approach is independent of the specific properties of the graph to be visualized, but certain graphs and tasks benefit from the asymmetry. As we show in our results, the strength of our technique is the visualization of dynamic graphs. In particular, sparse graphs benefit from the compact representation. Furthermore, our approach uses visual encoding by size to represent weights and therefore allows easy quantification and comparison. We evaluate our approach in a quantitative user study that confirms the suitability for dynamic and weighted graphs. Finally, we demonstrate our approach for two examples of dynamic graphs.

A Taxonomy and Survey of Dynamic Graph Visualization

“…Hlawatsch et al . [HBW14] provide empirical evidence that a list‐based approach can be superior to matrix and node‐link representations for certain tasks related to the degree of vertices and weights of edges, while lists perform on a comparable level for other tasks. Shi et al .…”

“…Hlawatsch et al . [HBW14] introduce two ways of using colour‐coded list representation to encode a dynamic graph on a horizontal timeline based on juxtaposition: First, each line represents a vertex and lists of links encoded as boxes are stacked horizontally (Figure , left). Second, each line represents a link containing a box if the link is active in a time step; lines belonging to the same vertex are stacked vertically (Figure , right).…”

“…Employing the colour of boxes to encode the source or target of an edge, some ambiguity is introduced, but high-level structures are preserved. Hlawatsch et al [HBW14] introduce two ways of using colour-coded list representation to encode a dynamic graph on a horizontal timeline based on juxtaposition: ( Figure 12, left). Second, each line represents a link containing a box if the link is active in a time step; lines belonging to the same vertex are stacked vertically (Figure 12, right).…”

“…Evaluating an approach using different timeline-based views connected by animated transitions, Smuc et al [SFW*14] observe diverse problem-solving strategies, which suggests that the different views are required but the integration of views needs to be as smooth as possible. Hlawatsch et al [HBW14] provide empirical evidence that a list-based approach can be superior to matrix and node-link representations for certain tasks related to the degree of vertices and weights of edges, while lists perform on a comparable level for other tasks. Shi et al [SWW*15] evaluate their integrated timeline approach for egocentric networks against a small multiples and an animated approach; they find, among other results, that for tasks analysing the temporal aspect of a focused node, their egocentric approach performed best regarding error rate and response time.…”

netflower: Dynamic Network Visualization for Data Journalists

“…Only a few visualization designs exist that tackle this problem: The TimeArcTree [GBD09] technique visualizes dynamic directed weighted networks and allows for the aggregation of nodes based on their hierarchy. Visual Adjacency List [HBW14] is a compact network visualization technique that encodes topology by color and also supports dynamic directed weighted networks. EgoSlider [WPZ*15] and egoLines [ZGC*16] are two approaches for egocentric social network analysis based on dynamic networks that can also have weights and direction.…”