Visualizing the Evolution of Communities in Dynamic Graphs

Abstract

The community structure of graphs is an important feature that gives insight into the high‐level organization of objects within the graph. In real‐world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline‐based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.

Module-based visualization of large-scale graph network data

“…They demonstrate the performance of Louvain on several large data sets. Moreover, dynamic graph evolution is achieved in Vehlow et al (2015) using community detection and a stream representation.…”

Hierarchical Spatio-temporal Visual Analysis of Cluster Evolution in Electrocorticography Data

“…To further support the comprehension of transitions between communities, alluvial diagrams [29] model the links between clusters in different vertical axes as split-merge ribbons [30,36]. This approach enhances the visual traceability of important cluster evolution patterns.…”

“…Through this view, analysts can compare and analyze modular signatures (cluster evolution patterns) over time and identify important time intervals and distinct brain states. The cluster evolution patterns are represented using a flow-based visualization [29,36] (G1) (alluvial diagram), where the clusters metaphorically flow like a river with split/merge tributaries from left to right.…”

“…Flowbased transition links Li,j, where i is the source community and j is the sink community, connect clusters to show changes in the community structure over time. We model these links as Bezier curves, to generate a continuous representation of the transition between successive communities [36]. Fig.…”

“…To determine such an ordering, we must take all the timesteps into consideration. Several methods have been proposed to compute such an ordering [36,31]. Our approach handles more timesteps by not considering the individual elements contained within clusters and dividing the sorting procedure into N individual blocks of T timesteps.…”