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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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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