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Deployment of social nets in multilayer model to identify key individuals using majority voting

  • Fozia Noor
  • Asadullah Shah
  • Mohammad Usman Akram
  • Shoab Ahmad Khan
Regular Paper
  • 91 Downloads

Abstract

Social web and social media are evidenced to be a rich source of user-generated social content. Social media includes multiple numbers of social dimensions represented by different social networks. The identification of important player in these real-world social networks has been in high emphasis due to its effectiveness in multiple disciplines, especially in law enforcement areas working on dark networks. Many algorithms have been proposed to identify key players according to the objective of interest using suitable network centrality measures. This paper proposes a new perspective of dealing with key player identification by redefining it as a problem of “Key Individual Identification,” across multiple social dimensions. Research deals with each social dimension as a layer in the multiple-layer social network model. The proposed technique extracts a number of features from each network based on social network analysis. The features are assembled to formulate a global feature set representing the behaviors of individuals in all networks individually. The technique then attempts to find key individuals using hybrid classifiers. The results from all classifiers are formulated, and the final decision of an individual to be part of the individual key set is based on majority voting. This novel technique gives good results on a number of known networks.

Keywords

Social network analysis Multilayer network Key player identification Ensemble classification Majority voting Ego network analysis 

References

  1. 1.
    Allard A, Noël PA, Dubé LJ et al (2009) Heterogeneous bond percolation on multitype networks with an application to epidemic dynamics. Phys Rev E 79:036113CrossRefGoogle Scholar
  2. 2.
    Al-Garadi MA, Varathan KD, Ravana SD et al (2016) Identifying the influential spreaders in multilayer interactions of online social networks. J Intell Fuzzy Syst 31(5):2721–2735CrossRefGoogle Scholar
  3. 3.
    Battiston F, Nicosia V, Latora V (2014) Structural measures for multiplex networks. Phys Rev E 89:032804CrossRefGoogle Scholar
  4. 4.
    Berlingerio M, Coscia M, Giannotti F et al (2011) The pursuit of hobbies: analysis of hubs in large multidimensional networks. J Comput Sci 2:223–237CrossRefGoogle Scholar
  5. 5.
    Berlingerio M, Coscia M, Giannotti F et al (2013) Multidimensional networks: foundations of structural analysis. WWW Internet Web Info Syst 16:567–593Google Scholar
  6. 6.
    Blondel VD, Guillaume JL, Lambiotte R et al (2008) Fast unfolding of communities in large networks. J Stat Mech 2008:P10008CrossRefGoogle Scholar
  7. 7.
    Bonacich P, Lloyd P (2001) Eigenvector-like measures of centrality for asymmetric relations. Soc Netw 23(3):191–201CrossRefGoogle Scholar
  8. 8.
    Borgatti S (2002) The key player problem, presented in the proceedings of the National Academy of Sciences Workshop on Terrorism. National Academy of Sciences, Washington DCGoogle Scholar
  9. 9.
    Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71MathSciNetCrossRefGoogle Scholar
  10. 10.
    Borgatti SP (2006) Identifying sets of key players in a social network. Comput Math Organ Theory 1:21–34CrossRefzbMATHGoogle Scholar
  11. 11.
    Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Soc Netw 28(4):466–484CrossRefGoogle Scholar
  12. 12.
    Borgatti SP, Mehra A, Brass DJ et al (2009) Network analysis in the social sciences. Science 323(5916):892–895CrossRefGoogle Scholar
  13. 13.
    Bothorel C, Cruz JD, Magnani M et al (2015) Clustering attributed graphs: models, measures, and methods. Netw Sci 3(3):408–444CrossRefGoogle Scholar
  14. 14.
    Bródka P, Stawiak P, Kazienko P (2011) Shortest path discovery in the multi-layered social network. In: Advances in social networks analysis and mining (ASONAM), IEEE, pp 497–501Google Scholar
  15. 15.
    Bródka P, Kazienko P, Musiał K et al (2012) Analysis of neighborhoods in multi-layered dynamic social networks. Int J Comput Intell Syst 5(3):582–596CrossRefGoogle Scholar
  16. 16.
    Burt RS, Schott T (1989) Relational contents in multiple network systems. Research Methods in Social Network Analysis. University of California, Irvine, pp 185–213Google Scholar
  17. 17.
    Butt WH, Akram MU, Khan SA et al (2014) Covert network analysis for key player detection and event prediction using a hybrid classifier. Sci World J 2014:615431.  https://doi.org/10.1155/2014/615431
  18. 18.
    Cai D, Shao Z, He X et al (2005) Community mining from multi-relational networks. In: 9th European conference on principles and practice of knowledge discovery in databasesGoogle Scholar
  19. 19.
    Capocci A, Servedio V, Colaiori F et al (2006) Preferential attachment in the growth of social networks: the internet encyclopedia Wikipedia. Phys Rev E 74(3):036116CrossRefGoogle Scholar
  20. 20.
    Chawla N, Bowyer K, Hall L et al (2000). SMOTE: synthetic minority over-sampling technique. In: International conference of knowledge based computer systems, National Center for Software Technology, Mumbai, India, Allied Press, pp 46–57Google Scholar
  21. 21.
    Cheng X, Dale C, Liu J (2008) Statistics and social networking of YouTube videos. In: 16th International workshop on quality of service, IEEE, pp 229–238Google Scholar
  22. 22.
    Conover MD, Gonçalves B, Ratkiewicz J et al (2011) Predicting the political alignment of Twitter users. In: Privacy, security, risk, and trust (passat), IEEE third international conference on social computing (socialcom), IEEE, pp 192–199Google Scholar
  23. 23.
    Coscia M, Rossetti G, Pennacchioli D et al (2013) You know because I know: a multidimensional network approach to human resources problem. In: IEEE/ACM international conference on advances in social networks analysis and mining, ACM, pp 434–441Google Scholar
  24. 24.
    De Domenico M, Sole-Ribalta A, Omodei E et al (2015) Ranking in interconnected multilayer networks reveals versatile nodes. Nat Commun 6:6868CrossRefGoogle Scholar
  25. 25.
    De Domenico M, Solé-Ribalta A, Omodei E et al (2015) Centrality in interconnected multilayer networks. Nat Commun 6:6868Google Scholar
  26. 26.
    De Domenico M, Porter MA, Arenas A (2015) Multilayer analysis and visualization of networks. J Complex Netw 3:159–176CrossRefGoogle Scholar
  27. 27.
    Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239MathSciNetCrossRefGoogle Scholar
  28. 28.
    Gluckman M (1955) The judicial process among the Barotse of Northern Rhodesia. Manchester University Press, ManchesterGoogle Scholar
  29. 29.
    Gómez S, Diaz-Guilera A, Gomez-Gardeñes G et al (2013) Diffusion dynamics on multiplex networks. Phys Rev Lett 110:028701CrossRefGoogle Scholar
  30. 30.
    Hage P, Harary F (1995) Eccentricity and centrality in networks. Soc Netw 17:57–63CrossRefGoogle Scholar
  31. 31.
    Halu A, Mondragon RJ, Panzarasa P et al (2013) Multiplex pagerank. PLoS ONE 8:e78293CrossRefGoogle Scholar
  32. 32.
    Harrer A, Schmidt A (2012) An approach for the block modeling in multi-relational networks. In: International conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 591–598Google Scholar
  33. 33.
    Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27CrossRefzbMATHGoogle Scholar
  34. 34.
    Holland PW, Leinhardt S (1975) Local structure in social networks. In: Heise D (ed) Sociological methodology. Jossey-Bass, San FranciscoGoogle Scholar
  35. 35.
    Hope T, Nishimura T, Takeda H (2006) An integrated method for social network extraction. In: 15th International conference on World Wide Web (WWW’06), New York, NY, USA, ACM, pp 845–846Google Scholar
  36. 36.
    Hristova D, Musolesi M, Mascolo C (2014) Keep your friends close and your Facebook friends closer: a multiplex network approach to the analysis of offline and online social ties. In: International conference on weblogs and social media (ICWSM). AAAIGoogle Scholar
  37. 37.
    Huberman B, Romero D, Wu F(2009) Social networks that matter: Twitter under the microscope. First Monday, 1–5. arXiv:0812.1045v1
  38. 38.
    Kazienko P, Musial K, Kajdanowicz T (2011) Multidimensional social network and its application to the social recommender system. IEEE Trans Syst Man Cybern A Syst Hum 41:746–759CrossRefGoogle Scholar
  39. 39.
    Kim J, Lee JG (2015) Community detection in multi-layer graphs: a survey. SIGMOD Rec ACM 44(3):37–48CrossRefGoogle Scholar
  40. 40.
    Kivelä M, Arenas A, Barthelemy M et al (2014) Multilayer networks. J Complex Netw 2:203–271CrossRefGoogle Scholar
  41. 41.
    Kolda TG, Bader BW, Kenny JP (2005) Higher order web link analysis using multilinear algebra. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), Houston, TX, pp 242–249Google Scholar
  42. 42.
    Krebs VE (2002) Mapping networks of terrorist cells. Connections 24(3):43–52Google Scholar
  43. 43.
    Ku L-W, Liang Y-T, Chen H-H (2006) Opinion extraction, summarization, and tracking in news and blog corpora. In: AAAI-CAAW’06Google Scholar
  44. 44.
    Lathia N, Hailes S, Capra L (2008) kNN CF: a temporal social network. In: ACM conference on recommender systems, ACM, pp 227–234Google Scholar
  45. 45.
    Lin dS, Chalupsky H (2008) Discovering and explaining abnormal nodes in semantic graphs. IEEE Trans Knowl Data Eng 20(8):1039–1052CrossRefGoogle Scholar
  46. 46.
    Liu W, Chen P-Y, Yeung S et al (2017) Principled multilayer network embedding. In: Data mining workshops (ICDMW), IEEEGoogle Scholar
  47. 47.
    Magnani M, Rossi L (2011) The ML-model for multi-layer social networks. In: Proceedings of 2011 ASONAM international conference on advances in social networks analysis and mining, Kaohsiung City, Taiwan, pp 5–12Google Scholar
  48. 48.
    Magnani M, Rossi L (2013) Pareto distance for multi-layer network analysis, social computing, behavioral-cultural modeling, and prediction (SBP). Lect Notes Comput Sci 7812:249–256CrossRefGoogle Scholar
  49. 49.
    McGuire RM (2012) Weighted key player problem for social network analysis. Dissertation, Air Force Institute of Technology, BiblioScholar. ISBN: 978-1288395736Google Scholar
  50. 50.
    Meizhu L, Qi Z, Qi L et al (2015). Identification of influential nodes in a network of networks. arXiv:1501.05714v1
  51. 51.
    Memon N, Harkiolakis N, Hicks DL (2008) Detecting high-value individuals in covert networks: 7/7 London Bombing Case Study. In: Proceedings of the IEEE/ACS international conference on computer systems and applications, pp 206–215Google Scholar
  52. 52.
    Memon N, Larsen HL, Hicks DL, Harkiolakis N (2008) Retracted: detecting hidden hierarchy in terrorist networks: some case studies. In: Yang CC et al (eds) Intelligence and security informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, BerlinGoogle Scholar
  53. 53.
    Memon N, Qureshi AR, Wiil UK et al (2009) Novel algorithms for subgroup detection in terrorist networks. In: Presented at the international conference on availability, reliability, and security, Fukuoka Institute of Technology, Fukuoka, JapanGoogle Scholar
  54. 54.
    Melville P, Sindhwani V, Lawrence R et al (2009) Machine learning for social media analytics. In: Machine learning symposium, New York Academy of Sciences, New York, November 2009. http://www.prem-melville.com/. Accessed Jan 03 2017
  55. 55.
    Michalski R, Kazienko P, Król D (2012) Predicting social network measures using machine learning approach. In: Advances in social networks analysis and mining (ASONAM), IEEE/ACMGoogle Scholar
  56. 56.
    Mucha PJ, Porter MA (2010) Communities in multislice voting networks. Chaos 20:041108CrossRefGoogle Scholar
  57. 57.
    Mucha PJ, Richardson T, Macon K et al (2010) Community structure in time-dependent, multiscale, and multiplex networks. Science 328:876–878MathSciNetCrossRefzbMATHGoogle Scholar
  58. 58.
    Nieminen J (1974) On the centrality in a graph. Scand J Psychol 15(1):332–336MathSciNetCrossRefGoogle Scholar
  59. 59.
    Pempek TA, Yermolayeva YA, Calvert SL (2009) College students’ social networking experiences on Facebook. J Appl Dev Psychol 30(3):227–238CrossRefGoogle Scholar
  60. 60.
    Radicchi F, Arenas A (2013) Abrupt transition in the structural formation of interconnected networks. Nat Phys 9:717–720CrossRefGoogle Scholar
  61. 61.
    Roberts N, Everton SF (2011) Roberts and Everton terrorist data: Noordin top terrorist network (subset). Machine-readable data fileGoogle Scholar
  62. 62.
    Sageman M (2004) Understanding terror networks. University of Pennsylvania Press, PennsylvaniaCrossRefGoogle Scholar
  63. 63.
    Salehi M, Sharma R, Marzolla M (2015) Spreading processes in multilayer networks. IEEE Trans Netw Sci Eng 2(2):65–83CrossRefGoogle Scholar
  64. 64.
    Scott J (2000) Social network analysis: a handbook. SAGE Publications, LondonGoogle Scholar
  65. 65.
    Solá L, Romance M, Criado R et al (2013) Eigenvector centrality of nodes in multiplex networks. Chaos 3:033131CrossRefzbMATHGoogle Scholar
  66. 66.
    Solé-Ribalta A, De Domenico M, Gómez S (2014) Centrality rankings in multiplex networks. In: ACM conference on web science, ACM, pp 149–155Google Scholar
  67. 67.
    Sparrow MK (1991) The application of network analysis to criminal intelligence: an assessment of the prospects. Soc Netw 13(3):251–274CrossRefGoogle Scholar
  68. 68.
    Tang X, Yang CC (2010) Generalizing terrorist social networks with k-nearest neighbor and edge-betweenness for social network integration and privacy preservation. In: International conference on intelligence and security informatics, IEEEGoogle Scholar
  69. 69.
    Tyler JR, Wilkinson DM, Huberman BA (2003) Email as spectroscopy: automated discovery of community structure within organizations. In: Huysman M (ed) Communities and technologies. Kluwer, B.V., Deventer, pp 81–96CrossRefGoogle Scholar
  70. 70.
    Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  71. 71.
    Wang P, Robins G, Pattison P et al (2013) Exponential random graph models for multilevel networks. Soc Netw 35:96–115CrossRefGoogle Scholar
  72. 72.
    Wang D, Wang H, Zou X (2017) Identifying key nodes in multilayer networks based on tensor decomposition. Chaos Interdisip J Nonlinear Sci 10(1063/1):4985185Google Scholar
  73. 73.
    Wiil UK, Memon N, Karampelas P (2010) Detecting new trends in terrorist networks. In: International conference on advances in social networks analysis and miningGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Fozia Noor
    • 1
    • 2
  • Asadullah Shah
    • 2
  • Mohammad Usman Akram
    • 3
  • Shoab Ahmad Khan
    • 3
  1. 1.Yanbu University College (YUC)YanbuSaudi Arabia
  2. 2.International Islamic University Malaysia (IIUM)GombakMalaysia
  3. 3.National University of Sciences and Technology (NUST)IslamabadPakistan

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