A model for analyzing dynamic social networks using system dynamics

Document Type : Original Article

Authors

1 PhD student, Department of Industrial Engineering, Faculty of Industrial Engineering, Imam Hossein University (AS), Tehran, Iran.

2 Assistant Professor, Department of Industrial Engineering, Faculty of Industrial Engineering, Imam Hossein Comprehensive University, Tehran, Iran

Abstract

One main problem in organizations is the lack of timely detection of large-scale changes such as crises. Detecting the changes is always delayed. One way to detect a crisis in an organization is to examine and analyze the social network of the employees. The models used so far in this field are generally graph-based and use the assumption of heterogeneity of nodes. Such models are not capable of modeling dynamic behaviors due to the static nature of the graphs. So far, no study has been found on the system dynamics approach in analyzing the behavior of longitudinal social networks. In order to fill this research gap, in this paper, using the system dynamics framework, dynamic social network modeling is done by considering the heterogeneity behavior of nodes. In this method, stochastic differential equation is used to simulate the collective behavior of nodes and to highlight the stochasticity of dynamic social networks. This modeling is simulated in Vensim software. In order to validate the proposed model, this modeling has been evaluated on Enron's communication network. Then, by examining the simulation scenarios and variables, it was found that the volatility rate or standard deviation of the data is a key variable in social networks. The results of this study provide a model of social media behavior in different situations for decision makers and planners.

Keywords

References

[1]     I. McCulloh, "Detecting Changes in a Dynamic Social Network," Institute for Software Research School of Computer Science Carnegie Mellon University Pittsburgh, pp. 1-10, 2009. 
[2]     T. Snijders, "Models for Longitudinal Network Data.," Models and Methods in Social Network Analysis., vol. 1, 2005. 
[3]     C. Joslyn and L. Rocha, "Towards semiotic agent-based models of socio-technical organizations, Proc.," AI, Simulation and Planning in High Autonomy Systems (AIS 2000) Conference, Tucson, Arizona,, pp. 70-79, 2000. 
[4]     A. Sanil, D. Banks and K. Carley, "Models for evolving fixed node networks: Model fitting and model testing," Social Networks, vol. 17, pp. 1-26, 1995. 
[5]     S. Feld, "Structural embeddedness and stability of interpersonal relations," Social Networks, vol. 19, pp. 91-95, 1997. 
[6]     C. Lund Pedersen and T. Ritter, "The market-shaping potential of a crisis," inudstrial Marketing Management, vol. 103, pp. 146-153, 2022. 
[7]     M. Gouse Galety, C. Al Atroshi, B. K. Balabantaray and S. Nandan Mohanty, Social Network Analysis: Theory and Applications, New Jersey: Wiley, 2022. 
[8]     m. azizi, A. Azar and m. Dehghan nayeri, "Applying PANDA Decision Making Strategy in Crisis: Focusing on the Corona Crisis in Iran," Management Research in Iran, vol. 24, no. 3, pp. 27-49, 1399. 
[9]     A. Karaivanov, "A social network model of COVID-19," PLoS ONE, vol. 15, no. 10, 2020. 
[10]     W.-K. Wang, W.-M. Lu, I. Wei Kiong Ting and Y.-H. Chen, "Social Networks and Dynamic Firm Performance: Evidence from the," REVISTA DE CONTABILIDAD SPANISH ACCOUNTING REVIEW, vol. 24, no. 1, pp. 62-74, 2021. 
[11]     D. N. Fisher and N. PINTER-WOLLMAN, "Using multilayer network analysis to explore," Current Zoology, vol. 67, no. 1, pp. 71-80, 2021. 
[12]     n. M. Niezink, T. A. Sniders and M. A. Van Duijn, "NO LONGER DISCRETE: MODELING THE DYNAMICS OF SOCIAL NETWORKS AND CONTINUOUS BEHAVIOR," Sociological Methodology, vol. 49, pp. 295-340, 2019. 
[13]     Snijders, "Statistical methods for network dynamics, In S. R. Luchini et al.," Proceedings of the XLIII Scientific Meeting, Italian Statistical Society, pp. 281-296, 2006. 
[14]     Growiec, J. Growiec and B. Kamiński, "Social Network Structure and The Trade-Off Between Social Utility and Economic Performance," Social Networks, vol. 55, pp. 31-46, 2018. 
[15]     H. Rahmanseresht, A. Tayebi Abolhasani and S. Rouhani Rad, "Analysis of Scientific Collaboration Networks of Researchers in the Field of Strategic Management in Iran," Management Research in Iran, vol. 23, no. 3, pp. 1-27, 1398. 
[16]     m. sabet sarvestani, A. Moghbel BaArz and a. azar, "Investigating the relationship between supply network structure and production efficiency: social network analysis and data envelopment analysis," Modern Researches in Decision Making, vol. 4, no. 2, pp. 175-197, 1398. 
[17]     m. sabet sarvestani, a. moghbel baarz and A. Afsar, "Comparative analysis of the structural attributes of supply network firms in auto industry (social network analysis approach)," Modern Researches in Decision Making, vol. 4, no. 4, pp. 59-80, 1398. 
[18]     P. a. R. A. Erdos, "On random graphs.," Publicationes Mathematicae Debrecen, vol. 6, pp. 290-297, 1959. 
[19]     E. Gilbert, "Random graphs," Annals of Mathematical Statistics, vol. 30, no. 4, pp. 1141-1144, 1959. 
[20]     T. Snijders, Discussion of M.S. Handcock, A. Raftery and J. Tantrum, "Model-based," Journal of Royal Statistical Society, vol. A170, pp. 322-324, 2007. 
[21]     Øksendal and Bernt., Stochastic Differential Equations. An Introduction with Applications, New York, 2000. 
[22]     A. Karadoğan and A. Karcı, "Marked social networks: A new model of social networks based on dynamic behaviors," Engineering Science and Technology, an International Journal, pp. 1-7, 2022. 
[23]     Sterman, Business Dynamics, Massachusett: McGrowhill, 2000. 
[24]     W. Kermack and A. McKendrick, "Contributions to the mathematical theory of epidemics," Proceedings of the Royal Society, vol. 115A, pp. 700-721, 1991. 
[25]     L. I. Andrade, "Análisis dinámico del comportamiento de las redes sociales," Universidad de Los Lagos; Chile, 2010. [Online]. Available: http://www.dinamica-de-sistemas.com/revista/1210a.htm, 2010.
[26]     Travers, Jeffrey, Milgram and Stanley, "An Experimental Study of the Small World Problem," Sociometry, vol. 32, no. 4, pp. 425-443, 1969. 
[27]     A. R. Benson, R. Adebe, M. T. Schaub, A. Jaldbabaie and J. Kleinberg, "Simplicial closure and higher-order link prediction," Proceedings of the National Academy of Sciences, vol. 115, no. 48, pp. 11221-11230, 2018. 
[28]     J. D. Wilson, N. T. Stevens and W. H. Woodall, "Modeling and detecting change in temporal networks via the degree corrected stochastic block model," Qual Reliab

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