Abstract
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.
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References
Moody, J., McFarland, D., Bender-deMoll, S.: Dynamic Network Visualization. American Journal of Sociology 110(4), 1206–1241 (2005)
Kolda, T.G., Bade, B.W.: Tensor Decompositions and Applications. SIAM Review 51(3), 455–500 (2009)
Smilde, A.K.: Three-way Analyses Problems and Prospects. Chemometrics and Intelligent Laboratory Systems 15, 143–157 (1992)
Tucker, L.: Some Mathematical Notes on Three-Mode Factor Analysis. Psychometrika 31(3), 279–311 (1966)
Kroonenberg, P.M.: Three-mode Principal Component Analysis: Theory and Applications. DSWO Press, Leiden (1983)
Aigner, W., Miksch, S., Muller, W., Schumann, H., Tominski, C.: Visualizing Time-Oriented Data - a Systematic View. Computers and Graphics 31, 401–409 (2007)
Oseledets, I., Savostyanov, D., Tyrtyshnikov, E.: Linear Algebra for Tensor Problems. Computing 85(3), 169–188 (2009)
Bader, B., Kolda, T.: MATLAB Tensor Toolbox Version 2.4 (March 2010), http://csmr.ca.sandia.gov/tgkolda/TensorToolbox/
Lavit, C., Escoufier, Y., Sabatier, R., Traissac, P.: The ACT (STATIS method). Computational Statistics and Data Analysis 18, 97–119 (1994)
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)
Van de Bunt, G.G., van Duijn, M.A.J., Snijders, T.A.B.: Friendship Networks through Time: An Actor-Oriented Statistical Network Model. Computational and Mathematical Organization Theory 5, 167–192 (1999)
Michell, L., Amos, A.: Girls, pecking order and smoking. Social Science and Medicine 44, 1861–1869 (1997)
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Oliveira, M., Gama, J. (2011). Visualizing the Evolution of Social Networks. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_35
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DOI: https://doi.org/10.1007/978-3-642-24769-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24768-2
Online ISBN: 978-3-642-24769-9
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