NDLI: Modeling dynamic social networks using spectral embedding

Content Provider	Springer Nature Link
Author	Skillicorn, D. B. Zheng, Q. Morselli, C.
Copyright Year	2014
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.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	18695450
Journal	Social Network Analysis and Mining
Volume Number	4
Issue Number	1
e-ISSN	18695469
Language	English
Publisher	Springer Vienna
Publisher Date	2014-03-21
Publisher Place	Vienna
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Temporal social networks Trajectories Directed Laplacians Drug smuggling networks Data Mining and Knowledge Discovery Complex Networks Game Theory, Economics, Social and Behav. Sciences Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Methodology of the Social Sciences
Content Type	Text
Resource Type	Article
Subject	Information Systems Human-Computer Interaction Communication Computer Science Applications Media Technology

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