TY - GEN
T1 - Social network extraction and high value individual (HVI) identification within fused intelligence data
AU - Farasat, Alireza
AU - Gross, Geoff
AU - Nagi, Rakesh
AU - Nikolaev, Alexander G.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - This paper reports on the utility of social network analysis methods in the data fusion domain. Given fused data that combines multiple intelligence reports from the same environment, social network extraction and High Value Individual (HVI) identification are of interest. The research on the feasibility of such activities may help not only in methodological developments in network science, but also, in testing and evaluation of fusion quality. This paper offers a methodology to extract a social network of individuals from fused data, captured as a Cumulative Associated Data Graph (CDG), with a supervised learning approach used for parameterizing the extraction algorithm. Ordered, centralitybased HVI lists are obtained from the CDGs constructed from the Sunni Criminal Thread and Bath’est Resurgence Threads of the SYNCOIN dataset, under various fusion system settings. The reported results shed light on the sensitivity of betweenness, closeness and degree centrality metrics to fused graph inputs and the role of HVI identification as a test-and-evaluation tool for fusion process optimization.
AB - This paper reports on the utility of social network analysis methods in the data fusion domain. Given fused data that combines multiple intelligence reports from the same environment, social network extraction and High Value Individual (HVI) identification are of interest. The research on the feasibility of such activities may help not only in methodological developments in network science, but also, in testing and evaluation of fusion quality. This paper offers a methodology to extract a social network of individuals from fused data, captured as a Cumulative Associated Data Graph (CDG), with a supervised learning approach used for parameterizing the extraction algorithm. Ordered, centralitybased HVI lists are obtained from the CDGs constructed from the Sunni Criminal Thread and Bath’est Resurgence Threads of the SYNCOIN dataset, under various fusion system settings. The reported results shed light on the sensitivity of betweenness, closeness and degree centrality metrics to fused graph inputs and the role of HVI identification as a test-and-evaluation tool for fusion process optimization.
KW - Social network analysis · Data fusion · Testing and evaluation · Centrality · High value individuals
UR - https://www.scopus.com/pages/publications/84925337384
U2 - 10.1007/978-3-319-16268-3_5
DO - 10.1007/978-3-319-16268-3_5
M3 - Conference contribution
AN - SCOPUS:84925337384
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 54
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
A2 - Xu, Kevin
A2 - Agarwal, Nitin
A2 - Osgood, Nathaniel
PB - Springer Verlag
T2 - 8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
Y2 - 31 March 2015 through 3 April 2015
ER -