Gerçek ve model ağların karakteristik özelliklerinin karşılaştırılması

Ağ analizi (network analysis) terimi uzun yıllara dayanan köklü bir geçmişe sahiptir. Ancak şu anda kullanıldığı biçimi bilgisayar teknolojilerindeki ilerlemelere paralel olarak ortaya çıkan, sosyal medya araçları sayesinde olmuştur. Ağ analizinin, farklı alanlarda değişik uygulamaları bulunmaktadır. Günümüzde, sosyal ağların yanı sıra ekonomik ağlar, teknolojik ağlar, fiziksel ve biyolojik ağlar da mevcuttur ve bu ağların, benzer yöntemlerle analizleri yapılabilir. Bu durum, çok disiplinli bir ağ terminolojisinin doğmasına sebep olmuştur. Bu çalışma, uzmanlık alanlarında ağ analizini uygulamak isteyen kişiler için, genel bir bilgi kaynağı olması amacıyla hazırlanmıştır. Çalışmada, ağların genel özellikleri farklı örnekler ile açıklanmış ve gösterilmiştir. Çalışmanın birinci bölümünde, pek çok düğüm ve bağlantıdan oluşan gerçek ağlar tanımlanmıştır. Bu ağların incelenmesinde verilerin toplanması ve sınırlarının belirlenmesinin önemi vurgulanmıştır. İkinci bölümde, gerçek ağların tanımı yapılarak gerçek ağların oluşturulmasında sınırların belirlenmesinden bahsedilmiştir. Üçüncü bölümde farklı ağ türlerini birbirinden ayıran önemli özellikler listelenerek açıklanmıştır. Dördüncü bölüm ise bilgisayarlarda çeşitli programlar ve algoritmalar yardımıyla üretilmiş olan sentetik ağlara ve ağ modellerine ayrılmıştır. Sentetik ağların, kullanım alanlarına ve özelliklerine bu bölümde yer verilmiştir.

Comparison of characteristics of real and modelled networks

The term of “Network Analysis” has a long history based on many years. However its current usage format is determined by means of the social media tools which came up in accordance with the developments in computer technologies.Nowadays network analysis has various applications in different areas including social, economical, techonological, physical and biological networks which can be analyzed with similar methods. This situation has formed the basis of multi-disciplinary network terminology. This study intends to be a general source of information for those who want to apply network analysis in their areas of expertise. In the study, general network features are explained and illustrated with different examples. In the first part of the study, real networks consisting of many different nodes and connections are identified. The importance of gathering information and determining limits in network analysis are emphasized. Second part includes real network definition and mentions determination of limits in formation of real network. The key features that seperate different network types are listed and described in the third part. The fourth part includes synthetic networks and network models created by different computer programs and algorithms. Usage areas and features of the synthetic networks are also explained in this section.

Kaynakça

[1] A.-L. Barabási, Linked: The new science of networks, vol. 71. Perseus Publishing, 2002, p. 123.

[2] D. J. Watts, The “New” Science of Networks, vol. 30, no. 1. 2004, pp. 243–270.

[3] T. G. Lewis, Network Science. 2009.

[4] Infographics, “Statistics for Social Media Sites,” 2012. [Online]. Available: http://cdn2.business2com- munity.com/wp-content/uploads/2012/12/Social-Growth-infographic4.png. [Accessed: 05-Feb- 2013].

[5] Central Intelligence Agency, “CIA - The World Factbook,” 2012. [Online]. Available: https://www.cia. gov/library/publications/the-world-factbook/geos/xx.html. [Accessed: 16-Feb-2013].

[6] D. Bennett, “The Dunbar Number, From the Guru of Social Networks,” Bloomberg Businessweek Tech- nology, pp. 1–6, 10-Jan-2013.

[7] V. Mahajan and R. Peterson, Models for innovation diffusion. 1985.

[8] T. Valente, “Social network thresholds in the diffusion of innovations,” Soc. Networks, vol. 18, no. 1, pp. 69–89, Jan. 1996.

[9] J. Cointet and C. Roth, “Information diffusion in realistic networks,” Main, pp. 1–4, 2007.

[10] E. M. E. Rogers, Diffusion of innovations. Free Pr, 1995.

[11] D. Easley and J. Kleinberg, Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge Univ Pr, 2010.

[12] R. Albert and A.-L. Barabási, “Statistical Mechanics of Complex Networks,” vol. 74, no. January, 2002.

[13] B. Schölkopf, “Structure and Dynamics of Information Pathways in Online Media,” 2013.

[14] S. H. Strogatz, “Exploring complex networks,” Nature, vol. 410, no. 6825, pp. 268–76, Mar. 2001.

[15] M. E. J. Newman, “Random graphs as models of networks,” Handb. graphs networks, no. 1, 2003.

[16] M. Mitchell, “Complex systems: Network thinking,” Artif. Intell., vol. 170, no. 18, pp. 1194–1212, 2006.

[17] C. Gros, Complex and adaptive dynamical systems, vol. 1. 2008, p. 2865.

[18] S. N. Dorogovtsev, “Evolution of networks,” 2001.

[19] A. Fronczak, P. Fronczak, and J. A. Holyst, “Average path length in random networks,” no. 3, pp. 1–4, 2008.

[20] M. E. J. Newman, “The Structure and Function of Complex Networks,” SIAM Rev., vol. 45, no. 2, pp. 167–256, Jan. 2003.

[21] M. O. Jackson, Social and Economic Networks. 2008.

[22] D. Radev and D. L. C. Mack, “Power Law Degree Distributions.” pp. 1–4, 2008.

[23] A.-L. Barabási, R. Albert, and H. Jeong, “Diameter of the World-Wide Web,” Nature, vol. 401, no. September, pp. 398–399, 1999.

[24] B. Huberman and L. Adamic, “Growth dynamics of the World-Wide Web,” Nature, vol. 401, p. 131, 1999.

[25] A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science (80-. )., vol. 286, no. October, pp. 509–512, 1999.

[26] D. Hutchison and J. C. Mitchell, Network Analysis. 2005.

[27] M. E. J. Newman, Networks: An Introduction. 2010.

[28] Network-science.org, “From sparsely to densely connected networks.” [Online]. Available: http:// www.network-science.org/highly-connected-society-dense-social-complex-networks.html. [Accessed: 19-Apr-2013].

[29] S. Milgram, “The small world problem,” Psychol. Today, vol. 1, no. 1, pp. 61–67, 1967.

[30] C. A. R. Pinheiro, Social network analysis in telecommunications. John Wiley & Sons, Inc., 2011, p. 95.

[31] A. Abraham, A.-E. Hassanien, and V. Snasel, Computational Social Network Analysis. 2010.

[32] K. M. Carley, J. Reminga, J. Storrick, and D. Columbus, “ORA User ’ s Guide 2010,” 2010.

[33] S. Bornholdt, H. Schuster, and J. Wiley, Handbook of graphs and networks. 2003.

[34] SOCNET Archives, “Assortativity, Homophily, Modularity,” 2009. [Online]. Available: http://lists.ufl. edu/cgi-bin/wa?A2=ind0905&L=SOCNET&D=0&P=27695. [Accessed: 25-Apr-2013].

[35] M. E. J. Newman, “Difference between assortativity, homophily, modularity,” 2009. [Online]. Available: http://zvonka.fmf.uni-lj.si/netbook/doku.php?id=private:socnet. [Accessed: 25-Apr-2013].

[36] P. J. Carrington, J. P. Scott, and S. Wasserman, Models and Methods in Social Network Analysis. 2005, p. 345.

[37] D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, and S. Suri, “Feedback effects between similarity and social influence in online communities,” Proceeding 14th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD ’08, p. 160, 2008.

[38] S. Wasserman and K. Faust, Social network analysis. 1994.

[39] M. McPherson, L. Smith-Lovin, and J. Cook, “Birds of a Feather : Homophily in Social Networks Source,” Annu. Rev. Sociol., vol. 27, no. 2001, pp. 415–444, 2001.

[40] B. Furht, Handbook of Social Network Technologies and Applications, vol. 129. Springer-Verlag New York Inc, 2010, p. 736.

[41] K. M. Carley, J. Reminga, J. Storrick, and D. Columbus, ORA User’s Guide 2011. 2011.

[42] A. T. Inc., “UCINET 6.3.0.1.” 2010.

[43] V. Batagelj and A. Mrvar, “Pajek 2.02.” 2010.

[44] Sonomine, “Sonomine 2.0.” 2010.

[45] D. Hansen, B. Shneiderman, and M. Smith, Analyzing social media networks with NodeXL. 2010.

[46] L. Hamill and N. Gilbert, “Simulating large social networks in agent-based models: A social circle model,” Emerg. Complex. Organ., vol. 12, no. 4, pp. 78–94, 2010.

[47] K. M. Carley and C. Team, “ORA Netscenes v3.0.0.2.” Carnegie-Mellon University, 2013.

[48] S. Havlin and R. Cohen, Complex Networks: Robustness and Function. 2010.

[49] M. J. Keeling and K. T. D. Eames, “Networks and epidemic models.,” J. R. Soc. Interface, vol. 2, no. 4, pp. 295–307, Sep. 2005.

[50] C. Y. Huang, C. T. Sun, and H. C. Lin, “Influence of local information on social simulations in small-world network models,” JASSSTHE J. Artif. Soc. Soc. Simul., vol. 8, no. 4, pp. 1–26, 2005.

[51] F. Alkemade and C. Castaldi, “Strategies for the Diffusion of Innovations on Social Networks,” Com- put. Econ., vol. 25, no. 1–2, pp. 3–23, Feb. 2005.

[52] X. F. Wang and G. Chen, “Complex Networks : Scale-Free and Beyond,” vol. 3, no. 2, 2003.

[53] C.-Y. Huang, “A Novel Small-World Model: Using Social Mirror Identities for Epidemic Simulations,” Simulation, vol. 81, no. 10, pp. 671–699, Oct. 2005.

[54] R. Kali, “Social Embeddedness and Economic Governance : A Small World Approach,” no. January, 2003.

[55] L. Hamill and N. Gilbert, “A simple but more realistic agent-based model of a social network,” Struc- ture, 2008.

[56] Q. Yan, L. Wu, and L. Zheng, “Social network based microblog user behavior analysis,” Phys. A Stat. Mech. its Appl., vol. 392, no. 7, pp. 1712–1723, Apr. 2013.

[57] M. E. J. Newman, “Assortative mixing in networks,” Phys. Rev. Lett., vol. 2, no. 4, pp. 1–5, 2002.

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