attention of the Western world and motivated a great deal of further research

Attention of the western world and motivated a great

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attention of the Western world and motivated a great deal of further research[ 23 29 ]. Much of this work focused on constructing networks based on intelligence and using the network’s topology to identify key individuals and evaluate intervention strategies. The rise of social media has introduced new opportunities for network science-based counter- terrorism, and some foresee social media intelligence (SOCMINT) as being a major intelligence source in the future [ 30 ]. This presents a fundamentally different counter- terrorism network science problem. Roughly, as opposed to using information about individuals to build networks, we now use networks to gain insight into individuals. Typically, we are also trying to identify a relatively small and possibly covert community within a much larger network. Such a change requires methodologies optimized to detect covert networks embedded in social media. The problem of community detection has been widely studied within the context of large- scale social networks [ 31 ]. Community detection algorithms attempt to identify groups of vertices more densely connected to one another than to the rest of the network. Social networks extracted from social media, however, present unique challenges due to their size and high clustering coefficients [ 32 ]. Furthermore, ties in online social networks like Twitter are widely recognized to represent different types of relationships [ 33 36 ]. Eaton and Mansbach [ 39 ] have introduced methods from constrained clustering literature to enable semi-supervised community optimization where a subset of vertices have known memberships as well. While such algorithms work well for certain classes of problems, community optimization algorithms have shown limited ability to detect threat
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networks embedded in social media when the group maintains connections with non- group members [ 35 ]. Community optimization is also unable to effectively account for multiplex graphs or graphs with multiple connection types. Like community optimization, graph partitioning finds partitions by minimizing intra-group connections; however, the number of groups, k , is fixed [ 31 ]. Covert network detection is then best described as a special case of graph partitioning where the partition is binary (or in other words, where k = 1) [ 40 ]. Smith et al. [ 40 ] effectively use this viewpoint to model spatiotemporal threat propagation using Bayesian inference, however their method does not extent to multiplex or multimode graphs when applied to social media. To do so, other methods must be used. In recent years, another sub-class of community detection methods has emerged, community detection in annotated networks. This body of work attempts to effectively incorporate node level attributes into clustering algorithms to account for noisiness of social networks embedded in social media. Vertex clustering originates from traditional data clustering methods and embeds graph vertices in a vector space where pairwise, Euclidian distances can be calculated [ 31 ]. In such approaches, a variety of eigenspace
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