Abstract
Dynamic social network analysis aims to understand the structures in networks as they evolve, as nodes appear and disappear, and as edge weights change. Working directly with a social network graph is difficult, and it has become standard to use spectral techniques that embed a graph in a geometry. Analysis can then be done in the geometry where distance approximates dissimilarity. Recently, spectral techniques have been extended to model directed graphs; we build on these techniques to model directed graphs that change over time. The snapshots of the social network at each time period are bound together into a single graph in a way that keeps structures aligned over time, and this global graph is then spectrally embedded. The similarities among a set of nodes can be tracked over time, so that changing relationships and clusters can be seen; and the concept of the trajectory of a node across time also becomes meaningful. We illustrate how these approaches can be used to understand the changing social network of the Caviar drug-trafficking network under both internal dynamics and response to law-enforcement actions.
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Skillicorn, D.B., Zheng, Q. & Morselli, C. Modeling dynamic social networks using spectral embedding. Soc. Netw. Anal. Min. 4, 182 (2014). https://doi.org/10.1007/s13278-014-0182-8
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DOI: https://doi.org/10.1007/s13278-014-0182-8