Abstract
Social network analysis is receiving an increased interest from multiple fields of science since more and more natural and synthetic networks are found to share similar features which help us understand their underlying topological properties. One desire is to create a model of the human society, however, the complexity of such a model is increased by the nature of human interaction, and present studies fail to create a fully realistic model of the societies we live in. Our approach is inspired from studies of online social networking and the ability of genetic algorithms (GA) to optimize topological data in a natural manner. We combine the properties of the small-world and scale-free models to create a community-based social network, which is then rearranged using empirically obtained data from Facebook friendship networks, and optimized using GAs. As a result, our synthetically generated social network topologies are more realistic, with a proposed realism fidelity metric that is with 63 % closer to the observed real-world parameters than the best existing model.
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References
Strogatz SH (2001) Exploring complex networks. Nature 410(6825):268–276
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442
Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Wang XF, Chen G (2003) Complex networks: small-world, scale-free and beyond. IEEE Circuits Syst Mag 3(1):6–20
Hoffmann AOI, Jager W, Von Eije JH (2007) Social simulation of stock markets: taking it to the next level. J Artif Soc Soc Simul 10(2)
Easley D, Kleinberg J (2010) Networks, crowds, and markets, vol 8. Cambridge University Press, Cambridge
Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47
Tsvetovat M, Carley KM (2005) Generation of realistic social network datasets for testing of analysis and simulation tools. DTIC Document, Technical report
Chen Y, Zhang L, Huang J (2007) The Watts-Strogatz network model developed by including degree distribution: theory and computer simulation. J Phys A: Math Theor 40(29):8237
Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: ICWSM
Rieder B (2013) Studying facebook via data extraction: the Netvizz application. In: Proceedings of the 5th annual ACM web science conference. ACM, pp 346–355
Kunegis J, Fay D, Bauckhage C (2010) Network growth and the spectral evolution model. In: Proceedings of the 19th ACM international conference on information and knowledge management. ACM, pp 739–748
Alberich R, Miro-Julia J, Rosselló F (2002) Marvel universe looks almost like a real social network. ArXiv preprint cond-mat/0202174
Leskovec J (2011) Stanford large network dataset collection. http://snap.stanford.edu/data/index.html
Milgram S (1967) The small world problem. Psychol Today 2(1):60–67
Csányi G, Szendrői B (2004) Structure of a large social network. Phys Rev E 69(3):036131
Arentze T, van den Berg P, Timmermans H (2012) Modeling social networks in geographic space: approach and empirical application. Environ Plan—Part A 44(5):1101
Costa LdF, Rodrigues FA, Travieso G, Villas Boas P (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242
Bigdeli A, Tizghadam A, Leon-Garcia A (2009) Comparison of network criticality, algebraic connectivity, and other graph metrics. In: Proceedings of the 1st annual workshop on simplifying complex network for practitioners. ACM, p 4
Amaral LAN, Scala A, Barthélémy M, Stanley HE (2000) Classes of small-world networks. Proc Natl Acad Sci 97(21):11 149–11 152
Kantarci B, Labatut V (2013) Classification of complex networks based on topological properties. In: Proceedings of the 3rd international conference on social computing and its applications
Li W, Yang J-Y (2009) Comparing networks from a data analysis perspective. Complex sciences. Springer, Berlin, pp 1907–1916
Caseiro N, Trigo P (2012) Comparing complex networks: an application to emergency managers mental models
Park K, Han Y, Lee Y-K (2013) An efficient method for computing similarity between frequent subgraphs. In: Proceedings of the 3rd international conference on social computing and its applications
Tan P-N et al (2007) Introduction to data mining. Pearson Education India, Upper Saddle River
Stigler SM (1989) Francis Galton’s account of the invention of correlation. Stat Sci 4(2):73–79
Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci (Calcutta) 2:49–55
Clark NR, Hu K, Chen EY, Duan Q, Maayan A (2013) Characteristic direction approach to identify differentially expressed genes. ArXiv preprint arXiv:1307.8366
Spertus E, Sahami M, Buyukkokten O (2005) Evaluating similarity measures: a large-scale study in the Orkut social network. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining. ACM, pp 678–684
Topirceanu A, Udrescu M, Vladutiu M (2013) Network fidelity: a metric to quantify the similarity and realism of complex networks. In: Proceedings of the 3rd international conference on social computing and its applications
Rana OF, Akram A, Lynden SJ (2005) Building scalable virtual communities: infrastructure requirements and computational costs. In: Socionics. Springer, Berlin, pp 68–83
Nakada T, Kato Y, Kunifuji S (2007) A study on the dynamics of friendship network formation using a directed network model
Mitchell M (1998) An introduction to genetic algorithms. MIT Press, Cambridge
Jacomy M, Heymann S, Venturini T, Bastian M (2011) Forceatlas2, a continuous graph layout algorithm for handy network visualization. Medialab Center of Research
Acknowledgments
This work was partially supported by the strategic grant POSDRU/159/1.5/S/ 137070 (2014) of the Ministry of National Education, Romania, co-financed by the European Social Fund—Investing in People, within the Sectoral Operational Programme Human Resources Development 2007–2013.
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Topirceanu, A., Udrescu, M., Vladutiu, M. (2014). Genetically Optimized Realistic Social Network Topology Inspired by Facebook. In: Kawash, J. (eds) Online Social Media Analysis and Visualization. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-13590-8_8
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