Visualization of evolving social networks using actor‐level and community‐level trajectories. Issue 4 (6th May 2013)

• Record Type: Journal Article
• Authors: Oliveira, Márcia; Gama, João
• Is Part Of: Expert systems. Volume 30:Issue 4(2013)
• Page Start: 306
• Page End: 319
• Publication Date: 2013-05-06
• Subjects: dynamic social networks -- network visualization -- trajectories -- Tucker3 model; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12028
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Visualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node‐link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node‐link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node‐link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node‐level and the community‐level, based on the concept of temporal trajectory. We resort to three‐order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self‐reported friendship networks.