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Focal structures analysis: identifying influential sets of individuals in a social network

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Abstract

Identifying influential individuals is a well-known approach in extracting actionable knowledge in a network. Existing studies suggest measures to identify influential individuals, i.e., they focus on the question “which individuals are best connected to others or have the most influence?”. Such individuals, however, may not represent the context (relationships, interactions, etc.) entirely in a social network. For example, it is nearly an impossible task for a single individual to organize a mass protest of the scale of the Saudi Arabian women’s 2013 Oct26Driving campaign, the 2012 Occupy Wall Street and the 2011 Arab Spring. Similarly, other events such as mobilizing the 2013 Taksim square-Gezi Park protesters, coordinating crisis response for natural disasters (e.g., the 2010 Haiti earthquake), or even organizing flash mobs would require a key set of individuals rather than a single or the most influential individual in a social network. An alternate line of research dealing with community or cluster identification approaches extract subnetworks of individuals. However, these structures may not represent the key sets of individuals that could coordinate the social processes mentioned above. Therefore, we develop the Focal Structures Analysis (FSA) methodology to extract such key sets of individuals, called focal structures, in a social network. This research goes beyond the traditional unit of analysis, which is an individual or a set of influential individuals, and places focal structures between the individuals and communities/clusters as the unit of analysis. To the best of our knowledge, this type of work is the first effort in identifying influential sets of individuals and would open up new directions for researchers to develop new methods in social network analysis.

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Notes

  1. http://insightradar.com/tr/realtime-visualization-of-gezi-protests-in-twitter/.

  2. http://www.focalstructures.net.

  3. http://www.focalstructures.net.

  4. http://www.merjek.com.

  5. http://tweettracker.fulton.asu.edu/.

  6. http://www.vox.com/cards/ukraine-everything-you-need-to-know/what-is-the-ukraine-crisis.

  7. http://www.buzzfeed.com/maxseddon/how-a-british-blogger-became-an-unlikely-star-of-the-ukraine#1qwp3ci.

References

  • Agarwal N, Galan M, Liu H, Subramanya S (2010) Wiscoll: collective wisdom based blog clustering. Inf Sci 180(1):39–61

    Article  Google Scholar 

  • Agarwal N, Liu H (2009) Modeling and data mining in blogosphere. Synth Lect Data Min Knowl Discov 1(1):1–109

    Article  Google Scholar 

  • Agarwal N, Liu H, Tang L, Philip SY (2012) Modeling blogger influence in a community. Soc Netw Anal Min 2(2):139–162

    Article  Google Scholar 

  • Barlow J, Rada R, Diaper D (1989) Interacting with computers. Interact Comput 1(1):39–42

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bordino I, Donato D, Gionis A, Leonardi S (2008) Mining large networks with subgraph counting. In: Data mining, 2008. ICDM’08. Eighth IEEE International Conference, IEEE, pp 737–742

  • Borgatti SP (1995) Centrality and aids. Connections 18(1):112–114

    Google Scholar 

  • Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71

    Article  Google Scholar 

  • Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30:107–117

    Article  Google Scholar 

  • Brooks CH, Montanez N (2006) Improved annotation of the blogosphere via autotagging and hierarchical clustering. In: Proceedings of the 15th International Conference on World Wide Web, ACM, pp 625–632

  • Burton K, Java A, Soboroff I (2009) The icwsm 2009 spinn3r dataset. In: Proceedings of the third annual conference on weblogs and social media (ICWSM 2009), San Jose, CA, 2009

  • Chi Y, Song X, Zhou D, Hino K, Tseng BL (2007a) Evolutionary spectral clustering by incorporating temporal smoothness. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 153–162

  • Chi Y, Zhu S, Song X, Tatemura J, Tseng BL (2007b) Structural and temporal analysis of the blogosphere through community factorization. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 163–172

  • Coleman JS (1989) Social capital in the creation of human capital. University of Chicago Press, Chicago

    Google Scholar 

  • Engagement rate: a metric you can count on. Retrieved March 2014 from http://www.socialbakers.com/blog/1427-engagement-rate-a-metric-you-can-count-on

  • Erds P, Renyi A (1959) On random graphs. Publ Math Debr 6:290–297

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  • Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Goshal G, Barabasi A-L (2011) Ranking stability and super-stable nodes in complex networks. Nat Commun 2:394

    Article  Google Scholar 

  • Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) Information diffusion through blogspace. In: Proceedings of the 13th international conference on World Wide Web, ACM, pp 491–501

  • Hagen L, Kahng AB (1992) New spectral methods for ratio cut partitioning and clustering. Comput Aided Des Integr Circuits Syst IEEE Trans 11(9):1074–1085

    Article  Google Scholar 

  • Haynes J, Perisic I (2009) Mapping search relevance to social networks. In Proceedings of the 3rd workshop on social network mining and analysis, Paris, France, ACM, New York, p 2

  • Holland PW, Leinhardt S (1971) Transitivity in structural models of small groups. Comp Group Stud 2:107–124

    Google Scholar 

  • Janssen J, Hurshman M, Kalyaniwalla N (2012) Model selection for social networks using graphlets. Internet Math 8(4):338–363

    Article  MathSciNet  Google Scholar 

  • Java A, Joshi A, Finin T (2008) Detecting communities via simultaneous clustering of graphs and folksonomies. In: Proceedings of WebKDD 2008

  • Kashtan N, Alon U (2005) Spontaneous evolution of modularity and network motifs. Proc Natl Acad Sc USA 102:13773–13778

    Article  Google Scholar 

  • Kashtan N, Itzkovitz S, Milo R, Alon U (2004) Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20:1746–1758

    Article  Google Scholar 

  • Kimura M, Saito K, Nakano R (2007) Extracting influential nodes for information diffusion on a social network. In: Proceedings of the 22nd national conference on artificial intelligence, vol 7, p 1371–1376, Vancouver, BC, Canada, July 22–26

  • Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893

    Article  Google Scholar 

  • Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM (JACM) 46(5):604–632

    Article  MathSciNet  MATH  Google Scholar 

  • Kondor R, Shervashidze N, Borgwardt KM (2009) The graphlet spectrum. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 529–536

  • Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007) Cascading behavior in large blog graphs. arXiv preprint. arXiv:0704.2803

  • Li B, Xu S, Zhang J (2007) Enhancing clustering blog documents by utilizing author/reader comments. In: Proceedings of the 45th annual southeast regional conference, ACM, pp 94–99

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

    Article  Google Scholar 

  • Newman ME (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133

    Article  Google Scholar 

  • Nieminen J (1974) On the centrality in a graph. Scand J Psychol 15(1):332–336

    Article  Google Scholar 

  • Ning H, Xu W, Chi Y, Gong Y, Huang TS (2007) Incremental spectral clustering with application to monitoring of evolving blog communities. In: SDM

  • Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: Bringing order to the web. Technical report 1999-66, Stanford University

  • Parsons T (1937) The structure of social action. In: Social action, New York: Free Press, pp 59–60

  • Portes A (2000) Social capital: its origins and applications in modern sociology. In: LESSER, Eric L (eds) Knowledge and social capital. Butterworth-Heinemann, Boston, pp 43–67

  • Pujol JM, Erramilli V, Rodriguez P (2009) Divide and conquer: partitioning online social networks. In: CoRR, abs/0905.4918

  • Rada R, Michailidis A (1995) Interactive media. Springer, New York

    Book  Google Scholar 

  • Şen F, Wigand RT, Agarwal N, Mahata D, Bisgin H (2012) Identifying focal patterns in social networks. In Computational aspects of social networks (CASoN), 2012 fourth international conference on IEEE, pp 105–108

  • Sen F, Nagisetty N, Viangteeravat T, Agarwal N (2015) An online platform for focal structures analysis-analyzing smaller and more pertinent groups using a web tool. In: AAAI spring symposium series. Stanford University, Palo Alto, CA, USA

  • Shervashidze N, Petri T, Mehlhorn K, Borgwardt KM, Vishwanathan S (2009) Efficient graphlet kernels for large graph comparison. In: International conference on artificial intelligence and statistics, pp 488–495

  • Shi J, Malik J (2000) Normalized cuts and image segmentation. Pattern Anal Mach Intell IEEE Trans 22(8):888–905

    Article  Google Scholar 

  • Turner JH (1988) A theory of social interaction. Stanford University Press, Palo Alto

    Google Scholar 

  • Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

    Article  MathSciNet  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442

    Article  Google Scholar 

  • Weber M (1978) Basic sociological terms. Econ soc 1:3–62

    Google Scholar 

  • Zachary W (1977) An information flow modelfor conflict and fission in small groups1. J Anthropol Res 33(4):452–473

    Article  Google Scholar 

  • Zaidi F (2013) Small world networks and clustered small world networks with random connectivity. Soc Netw Anal Min 1:1–13

    Google Scholar 

Download references

Acknowledgments

This work is supported in part by grant from the US Office of Naval Research (ONR) under award number N000141410489 and the US National Science Foundation (NSF) under award numbers IIS-1110868, IIS-1110649, and ACI-1429160.

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Correspondence to Fatih Şen Ph.D..

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Şen, F., Wigand, R., Agarwal, N. et al. Focal structures analysis: identifying influential sets of individuals in a social network. Soc. Netw. Anal. Min. 6, 17 (2016). https://doi.org/10.1007/s13278-016-0319-z

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  • DOI: https://doi.org/10.1007/s13278-016-0319-z

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