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A simulation method for social networks

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Abstract

With the increasing popularity of social networks, it is becoming more and more crucial for the decision makers to analyze and understand the evolution of these networks to identify for example, potential business opportunities. Unfortunately, understanding social networks, which are typically complex and dynamic, is not an easy task. In this paper, we propose an effective and practical approach for simulating social networks. We first develop a social network model that considers growth and connection mechanisms (including addition and deletion) of social networks. We consider the nodes’ in-degree, inter-nodes’ close degree which indicates how close the nodes are in the social network, which is limited by the in-degree threshold. We then develop a graph-based stratified random sampling algorithm for generating an initial network. To obtain the snapshots of a social network of the past, current and the future, we further develop a close degree algorithm and a close degree of estimation algorithm. The degree distribution of our model follows a power-law distribution with a “fat-tail”. Experimental results using real-life social networks show the effectiveness of our proposed simulation method.

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References

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459

    Article  Google Scholar 

  • Albert R, BarabJasi A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97

    Article  MATH  Google Scholar 

  • Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(15):509

    MathSciNet  Google Scholar 

  • Barabási A-L, Albert R, Jeong H (1999) Mean-field theory for scale free random networks. Phys A 272:173–187

    Article  Google Scholar 

  • Basso K, Margolin AA, Stolovitzky G et al (2005) Reverse engineering of regulatory networks in human B cells. Nat Genet 37(4):382–390

    Article  Google Scholar 

  • Bates JM, Granger CWJ (1969) The combination of forecast. Op Res Q 20:451–468

    Article  Google Scholar 

  • Bianconi G, Barabási AL (2001) Competition and multiscaling in evolving networks. EPL 54(4):436–440

    Article  Google Scholar 

  • Boyd CR, Tolson MA, Copes WS (1987) Evaluating trauma care: the TRISS method. Trauma score and the injury severity score. J Trauma 27(4):370–378

    Article  Google Scholar 

  • Bunn DWJ (1989) Forecasting with one model. Forecasting 8(3):162–166

    Google Scholar 

  • Carlson JM, Doyle J (1999) Highly optimized tolerance: a mechanism for power laws in designed systems. Phys Rev E 60(2):1412–1427

    Article  Google Scholar 

  • Che H, Gu J (2004) Cale-free network and system scientific significance. Syst Eng Theory Pract 4:11–16

    Google Scholar 

  • Chen K, Guo S (2010) Least absolute relative error estimation programme. J Am Stat Assoc 105(491):1104–1112

    Article  Google Scholar 

  • Chow T (1997) The Q-spectrum and spanning trees of tensor products of bipartite graphs. Proc Am Math Soc 125:3155–3161

    Article  MATH  Google Scholar 

  • Cooper C, Frieze A, Vera J (2004) Random deletion in a scale-free random graph process. Internet Math 1(4):463–483

    Article  MATH  MathSciNet  Google Scholar 

  • D’Angelo G, Ferretti S (2009) Simulation of scale-free networks. Belgium, ICST, 21

  • Deo N, Cami A (2007) Preferential deletion in dynamic models of web-like networks. Inf Process Lett 102:156–162

    Article  MATH  MathSciNet  Google Scholar 

  • Dorogovtsev SN, Mendes JFF (2000) Scaling behaviour of developing and decaying networks. Europhys Lett 52(1):33–39

    Article  Google Scholar 

  • Dorogovtsev SN, Mendes JFF, Samukhin AN (2000) Structure of growing networks with preferential linking. Phys Rev Lett 85:4633–4636

    Article  Google Scholar 

  • Fabrikant A, Koutsoupias E, Papadimitriou CH (2002) Heuristically optimized trade-offs: a new paradigm for power laws in the Internet. In Proceedings of the International Colloquium on Automata, Languages and Programming. Springer, pp 110–122

  • Feder T, Motwani R (1991) Clique partitions, graph compression and speeding-up algorithms. J Comp Sys Sci:123–133

  • Goel S, Salganik MJ (2009) Respondent-driven sampling as markov chain monte carlo. Stat Med 28:2202

    Article  MathSciNet  Google Scholar 

  • Hubler C, Kriegel HP, Borgwardt K, Ghahramani Z (2008) Metropolis algorithms for representative subgraph sampling. Proceedings of ICDM08, 283–292

  • Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL (2000) The large-scale organization of metabolic networks. Nature 407(6804):651–654

    Article  Google Scholar 

  • Leskovec J, Faloutsos C (2007) Scalable modelling of real graphs using kronecker multiplication. Proceedings of the 24 th International Conference

  • Leskovec J, Chakrabarti D, Kleinberg J, Faloutsos C (2005) Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication. http://www.cs.cmu.edu/~jure/pubs/kronecker-pkdd05.pdf

  • Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. Am Soc Inf Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092

    Article  Google Scholar 

  • Mitzenmacher M (2001) A brief history of lognormal and power law distributions. In Proceedings of the Allerton Conference on Communication, Control, and Computing, pp 182–191

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  MATH  MathSciNet  Google Scholar 

  • Novikov E, Barillot E (2008) Regulatory network reconstruction using an integral additive model with flexible kernel functions. Bmc Syst Biol 2:8

    Article  Google Scholar 

  • Pollner P, Palla G, Vicsek T (2006) Preferential attachment of communities: the same principle, but a higher level. Europhys Lett 73(3):478–484

    Article  MathSciNet  Google Scholar 

  • Ren W, Li J (2009) A fast algorithm from simulating scale-free networks, Proceedings of ICCT

  • Sabder J, Ester M, Kriegel HP, Xiaowei X (1998) Density-based clusteringin spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Disc 2(2):169–194

    Article  Google Scholar 

  • Sarshar N, Roychowdhury V (2004) Scale-free and stable structures in complex ad hoc networks. Phys Rev E 69(2):357–368

    Article  Google Scholar 

  • Squicciarini A, Rajasekaran SD, Mont MC (2011) Using modeling and simulation to evaluate enterprises’ risk exposure to social networks. IEEE Comput 44(1):66–73

    Article  Google Scholar 

  • SK Hacker, JT Israel (1999) Building trust key customer-supplier relationship. The performance center and satisfaction strategies

  • Toivonen R, Onnela JP (2006) A model for social networks. Phys A 371:851–860

    Article  Google Scholar 

  • Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6(3):324–342

    Article  MATH  MathSciNet  Google Scholar 

  • Yu J, Smith VA, Wang PP et al (2004) Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20(18):3594–3603

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Science and Technology, Yunnan Normal University, Kunming, China

    Rui Zeng

  2. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia

    Quan Z. Sheng & Lina Yao

Authors
  1. Rui Zeng
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  2. Quan Z. Sheng
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  3. Lina Yao
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Correspondence to Rui Zeng.

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Zeng, R., Sheng, Q.Z. & Yao, L. A simulation method for social networks. Soc. Netw. Anal. Min. 5, 6 (2015). https://doi.org/10.1007/s13278-015-0246-4

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  • DOI: https://doi.org/10.1007/s13278-015-0246-4

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