Visualizing the Evolution of Social Networks
In recent years we witnessed an impressive advance in the social networks field, which became a “hot” topic and a focus of considerable attention. Also, the development of methods that focus on the analysis and understanding of the evolution of data are gaining momentum. In this paper we present an approach to visualize the evolution of dynamic social networks by using Tucker decomposition and the concept of trajectory. Our visualization strategy is based on trajectories of network’s actors in a bidimensional space that preserves its structural properties. Furthermore, this approach can be used to identify similar actors by comparing the shape and position of the trajectories. To illustrate the proposed approach we conduct a case study using a set of temporal friendship networks.
KeywordsData Evolution Data Visualization Social Networks Trajectories Tucker3 model
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- 5.Kroonenberg, P.M.: Three-mode Principal Component Analysis: Theory and Applications. DSWO Press, Leiden (1983)Google Scholar
- 8.Bader, B., Kolda, T.: MATLAB Tensor Toolbox Version 2.4 (March 2010), http://csmr.ca.sandia.gov/tgkolda/TensorToolbox/
- 10.Sun, J., Papadimitriou, S., Lin, C., Cao, N., Liu, S., Qian, W.: Multivis: Content-based Social Network Exploration through Multi-way Visual Analysis. In: Proceedings of the 2009 SIAM International Conference on Data Mining (SDM 2009), pp.1063–1074 (2009)Google Scholar