Social network extraction and high value individual (HVI) identification within fused intelligence data

Alireza Farasat, Geoff Gross, Rakesh Nagi, Alexander G. Nikolaev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 8th International Conference, SBP 2015, Proceedings
EditorsKevin Xu, Nitin Agarwal, Nathaniel Osgood
PublisherSpringer-Verlag
Pages44-54
Number of pages11
ISBN (Electronic)9783319162676
DOIs
StatePublished - Jan 1 2015
Event8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015 - Washington, United States
Duration: Mar 31 2015Apr 3 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9021
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2015
CountryUnited States
CityWashington
Period3/31/154/3/15

Keywords

  • Social network analysis · Data fusion · Testing and evaluation · Centrality · High value individuals

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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