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Sosyal Ağ Analizi Üzerine Odaklanan Teorik Yapıların Karşılaştırılması

Year 2015, Volume: 3 Issue: 2, 1 - 17, 09.05.2016

Abstract

Son yıllarda, sosyal ağ kapsamında gerçekleşen bireysel olaylara ait kararların anlaşılması ve modellenmesi üzerine birçok çalışma yapılmaktadır. Bilgisayar temelli iletişim,  dijital veri depolama, Web 2.0 teknolojilerinin sunmuş olduğu kullanıcı etkileşimli dinamik yapı, sosyal ağ sitelerinin sunmuş olduğu yeni veri kaynakları ilginç araştırma sorularının ortaya çıkmasına neden olmuştur. Dolayısıyla, sosyal ağ yapılarının modellenmesi üzerine odaklanan sosyal ağ analizi alanında bilimsel çalışmalar yakın zamanda hız kazanmıştır. Çalışma, sosyal ağ yapılarının modellenmesi üzerine odaklanan yapıların karılaştırmalı olarak irdelendiği, teorik bir çalışmadır. Ele alınan modeller, genel kullanım alanlarına, kullanılan veri setlerinin yapılarına, modellerin bileşenlerine ve ele aldıkları değişim türlerine göre sınıflandırılmıştır. Çalışma, bu alanda yapılacak yeni çalışmalarda kullanılacak modellin saptanmasında yol gösterici niteliktedir.

Çalışmada, literatürde yer alan, sosyal yapılarının değerlendirilmesi ile ilgili dört önemli model ele alınmıştır. Multinominal logit model ilk kez ekonomistler tarafından tartışılmaya başlamış ve daha sonra sosyal ağ analizine odaklanan bazı modellerde sıklıkla kullanılmıştır. Yapısal ağ analizine odaklanan diğer modellerden üstel rasgele grafik modelleri (ERGMs), 1970'lerden günümüze sürekli geliştirilerek aktörler tarafından verilen kararların yapısal efektlerinin sunulmasında kullanılmaktadır. Stokastik aktör bazlı modeller ise, yapısal ağ kararlarını stokastik bir süreç içerisinde değerlendiren panel veri analizine dayalı, farklı parametre tahminlerine olanak tanıyan bir modellemedir. İlişkisel olay bazlı model, klasik olay bazlı modellemelerden uyarlanmış bir modelleme olup, son yıllarda önem kazanmıştır.   

Çalışmada, sosyal ağ yapılarının incelenmesi ile ilgili bahsi geçen modeller tarihsel bir akış içerisinde ele alınmış olup, teorik alt yapıları ile ilgili bilgilere yer verilerek karşılaştırılmalı olarak incelenmiştir. Bu sayede, sosyal ağlarda, ağ ve davranış dinamiklerinin incelenmesine odaklanacak gelecekteki çalışmalar için, seçim sürecinin modellenmesinde multinominal logit model, ağ değişiminin grafiksel sunumunda üstel rasgele grafik model, ağ ve davranış değişiminin sunulmasında stokastik aktör bazlı model ve aktöre değil olaya odaklanılan çalışmalarda ilişkisel olay çerçeveli modelin tercih üstünlüğünün olabileceği sonucuna varılmıştır.

 

Anahtar Kelimeler: Sosyal Ağ Analizi; Multinominal Logit Model; Üstel Rasgele Grafik Modelleme; Stokastik Aktör Bazlı Modelleme; İlişkisel Olay Bazlı Modelleme

References

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  • Snijders T.A.B., Steglich C.E.G., Schweinberger M. (2007), “Modeling the Co-Evolution of Networks and Behavior”, In: Longitudinal models in the behavioral and related sciences, edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum, pp.41-71.
  • Snijders T.A.B., Van de Bunt G. G., Steglich C.E.G. (2010), “ Introduction to Stohastic Actor-Based Models for Network Dynamics”, Social Networks, vol:32, page:44-60.
  • Steglich C,. Snijders T.A.B, Pearson M. (2010), “Dynamic Networks and Behavior: Separating Selection From Influence”, Sociological Methodology, vol:40, issue:1, page: 329-393.
  • Snijders T.A.B., (2011), “Statistical Models for Social Networks”, Annual Review of Sociology, vol: 37, page: 131–153.
  • Snijders T. A. B. (2011), “Network Dynamics” In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis, pp. 501–513. London.
  • -
  • Snijders T.A.B. ve Steglich C.E.G. (2013), “Representing Micro-Macro Linkages by Actor-based Dynamic Network Models”, Sociological Methodology and Research.
  • Snijders T.A.B.(2013), "Network Dynamics", In Rafael Wittek, Tom A.B. Snijders, and Victor Nee (eds.), The Handbook of Rational Choice Social Research, Stanford, CA: Stanford University Press, pp. 252-279.
  • -
  • Stadtfeld C. (2012), “Events in Social Networks”, KIT Scientific Publishing, Karlsruhe, Deutschland.
  • Ugander J., Backstrom L., Marlow C., ve Kleinberg J., (2012), “Structural Diversity in Social Contagion”, PNAS, vol: 109, no:16, page: 5962-5966.
  • Wasserman S. ve Faust K. (1994), “Social Network Analysis”, Cambridge University Press, UK.
  • Wasserman S., (1980), “Analyzing Social Networks as Stochastic Processes”, Journal of the American Statistical Association 75 (370), pp. 280–294.
Year 2015, Volume: 3 Issue: 2, 1 - 17, 09.05.2016

Abstract

References

  • Bauman K. E., Fisher L.A., Bryan E.S. ve Chenowenth R.L. (1984), “Antecedents Subjective Expected Utility and Behavior: A Study of Adolescent Cigarette Smoking”, addictive behaviors 9, p. 121-136.
  • Besag J. E., (1974), “Spatial Interaction and the Statistical Analysis of Lattice Systems”, Journal of the Royal Statistical Society, Series B (Methodological) 36 (2), pp. 192–236.
  • Box-Steffensmeier J. M., Jones B. S. (1997), “Time is of the Essence: Event History Models in Political Science”, American Journal of Political Science 41, pp. 1414-1461.
  • Brandes U., Lerner J., Snijders T.A.B. (2009), “Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data”, In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, IEEE Computer Society, pp. 200–205.
  • Burk W. J., Steglich C.E.G., ve Snijders T.A.B., (2007), “Beyond Dyadic Interdependence: Actor-Oriented Models for Co-Evolving Social Networks and Individual Behaviors”, International Journal of Behavioral Development 31 (4), pp. 397-404.
  • Butts C. T. (2008), “A Relational Event Framework for Social Action”, Sociological Methodology 38 (1), pp.155–200.
  • DesJardins S.L., Ahlburg D. A., McCall B. P. (1999), “ An Event History Model of Student Departure”, Economics of Education Review 18, pp. 375-390.
  • Fienberg S.E., Meyer M. M., Wasserman S., (1985), “Statistical Analysis of Multiple Socio-metric Relations”, Journal of the American Statistical Association 80, pp:51-67.
  • Frank O., Strauss D., (1986), “Markov Graphs”, Journal of the American Statistical Association, 81 (395), pp. 832–842.
  • Hoetker G.(2007), “ The Use of Logit Probit Models in Strategic Management Research: Critical Issues”, Strategic Management Journal, J: 28, pp. 331-343.
  • Holland P. W., Leinhardt S., (1981), “Exponential Family of Probability Distributions for Directed Graphs”, Journal of the American Statistical Association 76 (373), pp. 33–50.
  • Kapferer B (1972), “Strategy and Transaction in an African Factory”, Manchester University.
  • Katz N, Lazer D., Arrow H., ve Contractor N., (2004), “ Network Theory and Small Groups”, Small Group Research, vol: 35, no:3, page: 307-332.
  • Koskinen J., Edling C., (2012), “Modelling the Evolution of a Bipartite Network-Peer Referral in Interlocking Directorates”, Social Networks, vol: 34, page: 309-322.
  • Lubbers M. J., ve Snijders T.A.B. (2007), “A comparison of Various Approaches to the Exponential Random Graph Model: A Reanalysis of 102 Student Network in School Classes”, Social Networks 29, pp. 489-507.
  • Lusher D., Koskinen J., Robins ‬G. (2012), “Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications”‬, Cambridge University Press, UK.‬‬
  • ‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
  • McFadden D.(1974), “Conditional Logit Analysis Of Qualitative Choice Behavior”, In: Zarembka, P. (Ed.), Frontiers in Econometrics, Academic Press Inc., New York, Ch. 4, pp.105–142.
  • Mercken L., Steglich C., Sinclair P., Holliday J., Moore L., (2012), “A Longitudinal Social Network Analysis of Peer Influence, Peer Selection, and Smoking Behavior Among Adolescents in British Schools”, Health Psychology 31:4,pp. 450–459.
  • Nordlie P.G. (1958), “A Longitudinal Study of Interpersonal Attraction in a Natural Group Setting” Doktora Tezi, Michigan University.
  • Newcomb T.M (1961), “The Acquaintance Process”, New York: Holt, Rinehart ve Winston.
  • Robins G., Snijders T., Wang P., Handcock M., Pattison P. (2007), “Recent Developments in Exponential Random Graph (p*) Models for Social Networks”, Social Networks 29, pp. 192-215.
  • Salathe M., Q Vu D., Khandelwal S., ve Hunter D. R, (2013), “The Dynamics of Health Behavior Sentiments on Large Online Social Network”, EPJ Data Science, vol: 2, Springer.
  • Sampson S.F. (1969) “Crisis in a Cloister”, Doktora Tezi, Cornell Üniversitesi Network Models”, Simulating Social Phenomena, Springer, page: 493-512, Berlin.
  • -
  • Snijders T.A.B.(2001), “The Statistical Evaluation of Social Network Dynamics”, Sociological Methodology 31, pp. 361-395.
  • Snijders T.A. B. ve Baerveldt C. (2003), “A Multilevel Network Study of the Effects of Delinquent Behavior on Friendship Evolution”, Journal of Mathematical Sociology, vol: 27, page:123-151.
  • Steglich C,. Snijders T. A.B, Pearson M. (2006), “Dynamic Networks and Behavior: Separating Selection From Influence”, Sociological Methodology, Interuniversity Center for Social Science Theory and Methodology,Napier University, Edinburgh.
  • Snijders T.A.B., Steglich C.E.G., Schweinberger M. (2007), “Modeling the Co-Evolution of Networks and Behavior”, In: Longitudinal models in the behavioral and related sciences, edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum, pp.41-71.
  • Snijders T.A.B., Van de Bunt G. G., Steglich C.E.G. (2010), “ Introduction to Stohastic Actor-Based Models for Network Dynamics”, Social Networks, vol:32, page:44-60.
  • Steglich C,. Snijders T.A.B, Pearson M. (2010), “Dynamic Networks and Behavior: Separating Selection From Influence”, Sociological Methodology, vol:40, issue:1, page: 329-393.
  • Snijders T.A.B., (2011), “Statistical Models for Social Networks”, Annual Review of Sociology, vol: 37, page: 131–153.
  • Snijders T. A. B. (2011), “Network Dynamics” In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis, pp. 501–513. London.
  • -
  • Snijders T.A.B. ve Steglich C.E.G. (2013), “Representing Micro-Macro Linkages by Actor-based Dynamic Network Models”, Sociological Methodology and Research.
  • Snijders T.A.B.(2013), "Network Dynamics", In Rafael Wittek, Tom A.B. Snijders, and Victor Nee (eds.), The Handbook of Rational Choice Social Research, Stanford, CA: Stanford University Press, pp. 252-279.
  • -
  • Stadtfeld C. (2012), “Events in Social Networks”, KIT Scientific Publishing, Karlsruhe, Deutschland.
  • Ugander J., Backstrom L., Marlow C., ve Kleinberg J., (2012), “Structural Diversity in Social Contagion”, PNAS, vol: 109, no:16, page: 5962-5966.
  • Wasserman S. ve Faust K. (1994), “Social Network Analysis”, Cambridge University Press, UK.
  • Wasserman S., (1980), “Analyzing Social Networks as Stochastic Processes”, Journal of the American Statistical Association 75 (370), pp. 280–294.
There are 41 citations in total.

Details

Primary Language Turkish
Journal Section Makale
Authors

Keziban Seçkin Codal This is me

Erman Coşkun

Publication Date May 9, 2016
Submission Date October 1, 2015
Published in Issue Year 2015 Volume: 3 Issue: 2

Cite

APA Seçkin Codal, K., & Coşkun, E. (2016). Sosyal Ağ Analizi Üzerine Odaklanan Teorik Yapıların Karşılaştırılması. İşletme Bilimi Dergisi, 3(2), 1-17. https://doi.org/10.22139/ibd.38284