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Social Network Extraction and High Value Individual (HVI) Identification within Fused Intelligence Data

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Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9021))

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, centrality-based 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.

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References

  1. Arulselvan, A., Commander, C.W., Elefteriadou, L., Pardalos, P.M.: Detecting critical nodes in sparse graphs. Computers & Operations Research 36(7), 2193–2200 (2009)

    Google Scholar 

  2. Bastian, M., Heymann, S., Jacomy, M., et al.: Gephi: an open source software for exploring and manipulating networks. ICWSM 8, 361–362 (2009)

    Google Scholar 

  3. Borgatti, S.P.: Identifying sets of key players in a social network. Computational & Mathematical Organization Theory 12(1), 21–34 (2006)

    Google Scholar 

  4. Brandes, U.: A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25(2), 163–177 (2001)

    Article  MATH  Google Scholar 

  5. Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., Zhou, T.: Identifying influential nodes in complex networks. Physica a: Statistical Mechanics and its Applications 391(4), 1777–1787 (2012)

    Article  Google Scholar 

  6. Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM Journal on Discrete Mathematics 17(1), 134–160 (2003)

    Google Scholar 

  7. Graham, J.L., Hall, D.L., Rimland, J.: A synthetic dataset for evaluating soft and hard fusion algorithms. SPIE Defense, Security, and Sensing International Society for Optics and Photonics, pp. 80620F–80620F (2011)

    Google Scholar 

  8. Gross, G., Nagi, R., Sambhoos, K.: A fuzzy graph matching approach in intelligence analysis and maintenance of continuous situational awareness. Information Fusion 18, 43–61 (2014)

    Article  Google Scholar 

  9. Gross, G.A., Khopkar, S., Nagi, R., Sambhoos, K., et al.: Data association and graph analytical processing of hard and soft intelligence data. In: 2013 16th International Conference on Information Fusion (FUSION), pp. 404–411. IEEE (2013)

    Google Scholar 

  10. Gross, G.A., Nagi, R., Sambhoos, K., Schlegel, D.R., Shapiro, S.C., Tauer, G.: Towards hard+soft data fusion: Processing architecture and implementation for the joint fusion and analysis of hard and soft intelligence data. In: 2012 15th International Conference on Information Fusion (FUSION), pp. 955–962. IEEE (2012)

    Google Scholar 

  11. Kiss, C., Bichler, M.: Identification of influencers - measuring influence in customer networks. Decision Support Systems 46(1), 233–253 (2008)

    Article  Google Scholar 

  12. Llinas, J., Nagi, R., Hall, D., Lavery, J.: A multi-disciplinary university research initiative in hard and soft information fusion: Overview, research strategies and initial results. In: 2010 13th Conference on Information Fusion (FUSION), pp. 1–7. IEEE (2010)

    Google Scholar 

  13. Macindoe, O., Richards, W.: Graph comparison using fine structure analysis. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 193–200. IEEE (2010)

    Google Scholar 

  14. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  15. Mitchell Peabody, Finding groups of graphs in databases, Ph.D. thesis, Citeseer (2002)

    Google Scholar 

  16. Robins, G., Pattison, P., Kalish, Y., Lusher, D.: An introduction to exponential random graph p-star models for social networks. Social Networks 29(2), 173–191 (2007)

    Article  Google Scholar 

  17. Shervashidze, N., Borgwardt, K.M.: Fast subtree kernels on graphs. Advances in Neural Information Processing Systems, 1660–1668 (2009)

    Google Scholar 

  18. Shetty, J., Adibi, J.: Discovering important nodes through graph entropy the case of enron email database. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 74–81. ACM (2005)

    Google Scholar 

  19. Rama Suri, N., Narahari, Y.: Determining the top-k nodes in social networks using the shapley value. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 3. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1509–1512 (2008)

    Google Scholar 

  20. Tang, J., Musolesi, M., Mascolo, C., Latora, V., Nicosia, V.: Analysing information flows and key mediators through temporal centrality metrics. In: Proceedings of the 3rd Workshop on Social Network Systems, p. 3. ACM (2010)

    Google Scholar 

  21. Thelwall, M., Wilkinson, D., Uppal, S.: Data mining emotion in social network communication: Gender differences in myspace. Journal of the American Society for Information Science and Technology 61(1), 190–199 (2010)

    Article  Google Scholar 

  22. van der Aalst, W.M.P., Song, M.S.: Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  23. Varlamis, I., Eirinaki, M., Louta, M.: A study on social network metrics and their application in trust networks. 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 168–175. IEEE (2010)

    Google Scholar 

  24. Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 981–990. ACM (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, NY, USA

    Alireza Farasat, Geoff Gross & Alexander G. Nikolaev

  2. Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Rakesh Nagi

Authors
  1. Alireza Farasat
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  2. Geoff Gross
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  3. Rakesh Nagi
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  4. Alexander G. Nikolaev
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Corresponding author

Correspondence to Alexander G. Nikolaev .

Editor information

Editors and Affiliations

  1. University of Arkansas, Little Rock, Arkansas, USA

    Nitin Agarwal

  2. Technicolor Research, Los Altos, California, USA

    Kevin Xu

  3. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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© 2015 Springer International Publishing Switzerland

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Farasat, A., Gross, G., Nagi, R., Nikolaev, A.G. (2015). Social Network Extraction and High Value Individual (HVI) Identification within Fused Intelligence Data. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-16268-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16267-6

  • Online ISBN: 978-3-319-16268-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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