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Social Network Measures and Analysis

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Deviance in Social Media and Social Cyber Forensics

Part of the book series: SpringerBriefs in Cybersecurity ((BRIEFSCYBER))

Abstract

In this chapter, we present basic terminologies and concepts of graph theory in addition to a few social network measures that will be used throughout the book. Then we explain more advanced metrics and concepts that would leverage the basic network measures such as estimating blogs and bloggers’ influence scores and focal structures analysis (FSA). These concepts were used in many real-world cases to find coordinating sets of individuals (coordinating groups) in a given graph. All the concepts and measures are described and illustrated with examples. This chapter would provide the readers with basic understanding of graph-theoretic concepts and social network measures that will help understand the concepts of social cyber forensics in the later chapters.

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Notes

  1. 1.

    \(K\ddot {o}nigsberg\) is a city formerly a part of Germany and now it is a part of Russia known as Kaliningrad .

  2. 2.

    A graph consists of vertices connected by edges.

  3. 3.

    A network consists of nodes connected by links.

  4. 4.

    A sociogram consists of actors/points connected by relations.

  5. 5.

    K refers to the number of vertices types in the graph.

  6. 6.

    Q. How many edges are there in a complete and directed graph with N vertices? Ans: (N 2 − N).

  7. 7.

    The small world experiment conducted by the American social psychologist Stanley Milgram in 1967 leverage this concept. In his experiment, he measured the average path length between people in the USA. He found that on average any two randomly selected people living in the USA are connected by 5.5 (or, 6) hops [19]. The phrase “six degrees of separation” is associated with his experiment although he didn’t use this phrase.

  8. 8.

    An ego-alter network is a network in which you are called the “ego” and your friends are called the “alters” [20].

  9. 9.

    Small world networks are known to have a high global clustering coefficient and an average shortest path length that increase slowly as the number of vertices increases. There are many examples of small world networks such as the electric power grids, networks of word co-occurrence, and the biological neural networks just to name a few.

  10. 10.

    Most services are discontinued, e.g., Blogdex (developed by MIT and was shutdown in 2006), BlogPulse (developed by IntelliSeek and was shutdown in 2012), BlogScope (developed by the University of Toronto and was shutdown in 2012), Google Blog Search (BETA) (developed by Google and was shutdown in 2014), and Technorati (developed by Technorati and was shutdown in 2014) [34].

References

  1. L. Mastin, 18th Century Mathematics - Euler (2010), [Online]. Available: http://www.storyofmathematics.com/18th_euler.html

  2. E.W. Weisstein, Knigsberg Bridge Problem (2018), [Online]. Available: http://mathworld.wolfram.com/KoenigsbergBridgeProblem.html

  3. B. Doerr, M. Fouz, T. Friedrich, Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012)

    Article  Google Scholar 

  4. J.H. Fowler, N.A. Christakis, Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham heart study. BMJ 337, a2338 (2008)

    Article  Google Scholar 

  5. S. Fortunato, Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  6. O. Mason, M. Verwoerd, Graph theory and networks in biology. IET Syst. Biol. 1(2), 89–119 (2007)

    Article  Google Scholar 

  7. M.J. Keeling, K.T. Eames, Networks and epidemic models. J. R. Soc. Interface 2(4), 295–307 (2005)

    Article  Google Scholar 

  8. A.T. Balaban, Applications of graph theory in chemistry. J. Chem. Inf. Comput. Sci. 25(3), 334–343 (1985)

    Article  Google Scholar 

  9. R. Zafarani, M.A. Abbasi, H. Liu, Social Media Mining: An Introduction (Cambridge University Press, Cambridge, 2014)

    Book  Google Scholar 

  10. E.W. Weisstein, K-Partite Graph (2018). [Online]. Available: http://mathworld.wolfram.com/k-PartiteGraph.html

  11. M. Timilsina, I. Hulpus, C. Hayes, B. Davis, Heterogeneous Graphs for Academic Impact Assessment, Amsterdam, Netherlands (2015). [Online]. Available: http://elsevier.kdu.insight-centre.org/wp-content/uploads/altmetrics_2015_poster.pdf

    Google Scholar 

  12. S. Wasserman, K. Faust, Social Network Analysis: Methods and Applications, vol. 8 (Cambridge University Press, Cambridge, 1994)

    Book  Google Scholar 

  13. D. Snyder, E.L. Kick, Structural position in the world system and economic growth, 1955–1970: a multiple-network analysis of transnational interactions. Am. J. Sociol. 84(5), 1096–1126 (1979)

    Article  Google Scholar 

  14. R.J. Nemeth, D.A. Smith, International trade and world-system structure: a multiple network analysis. Review (Fernand Braudel Center) 8(4), 517–560 (1985)

    Google Scholar 

  15. L.A. Goodman, Criteria for determining whether certain categories in a cross-classification table should be combined, with special reference to occupational categories in an occupational mobility table. Am. J. Sociol. 87(3), 612–650 (1981)

    Article  Google Scholar 

  16. B. Kapferer, Norms and the Manipulation of Relationships in a Work Context (Manchester University Press, Manchester, 1969)

    Google Scholar 

  17. W.W. Zachary, An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)

    Article  Google Scholar 

  18. B. Thurman, In the office: networks and coalitions. Soc. Networks 2(1), 47–63 (1979)

    Article  Google Scholar 

  19. J. Travers, S. Milgram, The small world problem. Phys. Today 1(1), 61–67 (1967)

    Google Scholar 

  20. R. DeJordy, D. Halgin, Introduction to Ego Network Analysis (Boston College and the Winston Center for Leadership & Ethics, Boston, 2008)

    Google Scholar 

  21. G. Simmel, Sociology: Investigations on the Forms of Sociation (Duncker & Humblot, Berlin, 1908)

    Google Scholar 

  22. R.D. Luce, A.D. Perry, A method of matrix analysis of group structure. Psychometrika 14(2), 95–116 (1949)

    Article  MathSciNet  Google Scholar 

  23. D.J. Watts, S.H. Strogatz, Collective dynamics of small-world networks. Nature 393(6684), 440 (1998)

    Google Scholar 

  24. M. Girvan, M.E.J. Newman, Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002). [Online]. Available: http://www.pnas.org/content/99/12/7821.abstract

  25. M.E. Newman, Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006). [Online]. Available: http://www.pnas.org/content/103/23/8577.short

  26. D. Gillmor, We the media: the rise of citizen journalists. Natl. Civ. Rev. 93(3), 58–63 (2004)

    Article  Google Scholar 

  27. B. Etling, J. Kelly, R. Faris, J. Palfrey, Mapping the Arabic blogosphere: politics, culture, and dissent, in Media Evolution on the Eve of the Arab Spring (Springer, New York, 2014), pp. 49–74

    Book  Google Scholar 

  28. E.L. Mead, M.N. Hussain, M. Nooman, S. Al-khateeb, N. Agarwal, Assessing situation awareness through blogosphere: a case study on Venezuelan socio-political crisis and the migrant influx, in The Seventh International Conference on Social Media Technologies, Communication, and Informatics (SOTICS 2017) (The International Academy, Research and Industry Association (IARIA), 2017), pp. 22–29

    Google Scholar 

  29. N. Agarwal, H. Liu, L. Tang, P.S. Yu, Identifying the influential bloggers in a community, in Proceedings of the 2008 International Conference on Web Search and Data Mining (ACM, New York, 2008), pp. 207–218

    Google Scholar 

  30. N. Agarwal, H. Liu, L. Tang, S.Y. Philip, Modeling blogger influence in a community. Soc. Netw. Anal. Min. 2(2), 139–162. [Online]. Available: http://bit.ly/2mOw8HM

  31. S. Kumar, R. Zafarani, M.A. Abbasi, G. Barbier, H. Liu, Convergence of influential bloggers for topic discovery in the blogosphere, in Advances in Social Computing (Springer, Berlin, 2010), pp. 406–412. [Online]. Available: http://bit.ly/2nxdHaA

  32. A. Java, P. Kolari, T. Finin, T. Oates, Modeling the spread of influence on the blogosphere, in Proceedings of the 15th International World Wide Web Conference (2006), pp. 22–26. [Online]. Available: http://bit.ly/2nf4ZAA

  33. K.E. Gill, How can we measure the influence of the blogosphere, in WWW 2004 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics (Citeseer, New York, 2004). [Online]. Available: http://bit.ly/2nt8bcs

    Google Scholar 

  34. M.N. Hussain, A. Obadimu, K.K. Bandeli, M. Nooman, S. Al-khateeb, N. Agarwal, A framework for blog data collection: challenges and opportunities, in The IARIA International Symposium on Designing, Validating, and Using Datasets (DATASETS 2017) (The International Academy, Research and Industry Association (IARIA), 2017)

    Google Scholar 

  35. F. Şen, R. Wigand, N. Agarwal, S. Tokdemir, R. Kasprzyk, Focal structures analysis: Identifying influential sets of individuals in a social network. Soc. Netw. Anal. Min. 6(1), 1–22 (2016). [Online]. Available: http://bit.ly/1qS8Y4D

  36. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech Theory Exp. 2008(10), P10008 (2008)

    Google Scholar 

  37. P. Jaccard, The distribution of the flora in the alpine zone. New Phytol. 11(2), 37–50 (1912)

    Article  Google Scholar 

  38. F. Sen, N. Nagisetty, T. Viangteeravat, N. Agarwal, An online platform for focal structures analysis-analyzing smaller and more pertinent groups using a web tool, in 2015 AAAI Spring Symposium Series (2015)

    Google Scholar 

  39. S. Yuce, N. Agarwal, R.T. Wigand, M. Lim, R.S. Robinson, Studying the evolution of online collective action: Saudi Arabian womens oct26drivingtwitter campaign, in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (Springer, Cham, 2014), pp. 413–420

    Google Scholar 

  40. M. Seddon, How a British Blogger Became an Unlikely Star of the Ukraine Conflict and Russia Today, (BuzzFeed News, New York, 2014). [Online]. Available: http://bzfd.it/1qpuL2z

    Google Scholar 

  41. S. Al-khateeb, N. Agarwal, R. Galeano, R. Goolsby, Examining the use of botnets and their evolution in propaganda dissemination. Def. Strateg. Commun. 2(1), 87–112 (2017)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Journalism, Media & Computing, Creighton University, Omaha, NE, USA

    Samer Al-khateeb

  2. Information Science Department, University of Arkansas at Little Rock, Little Rock, AR, USA

    Nitin Agarwal

Authors
  1. Samer Al-khateeb
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  2. Nitin Agarwal
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Al-khateeb, S., Agarwal, N. (2019). Social Network Measures and Analysis. In: Deviance in Social Media and Social Cyber Forensics. SpringerBriefs in Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-030-13690-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-13690-1_2

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  • Online ISBN: 978-3-030-13690-1

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