Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T22:02:45.289Z Has data issue: false hasContentIssue false
  • English
  • Français

Creating context for social influence processes in multiplex networks

Published online by Cambridge University Press:  01 February 2017

J. ANTONIO RIVERO OSTOIC
J. ANTONIO RIVERO OSTOIC*
Affiliation:
School of Business and Social Sciences, Aarhus University, DK-8210 Aarhus V, Denmark (e-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper elaborates on two theories of social influence processes to multiplex network structures. First, cohesion influence is based on mutual communication made by different types of relations, and second comparison influence that is built on contrasting types of tie. While a system of bundles with a mutual character constitutes the setting for a multiplex network exposure measure within cohesion, comparison influence is defined algebraically through classes of actors in terms of a weakly balanced semiring structure that considers positive, negative, and also ambivalent types of tie. A case study with these approaches is made on an entrepreneurial community network with formal business relations, informal friendship ties, and perceived competition among the firms, and the methods are validated with the Sampson Monastery data set.

Keywords

multiplex networksnetwork exposurestructural balancebundle patternssemiringsinnovation adoption
Type
Research Article
Information
Network Science , Volume 5 , Issue 1 , March 2017 , pp. 1 - 29
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Back, K. (1951). Influence through social communication. Journal of Abnormal and Social Psychology, 46 (1), 9–23.CrossRefGoogle ScholarPubMed
Bakshy, E., Messing, S., & Adamic, L. (2015). Exposure to ideologically diverse news and opinion on facebook. Science, 348 (6239), 1130–1132.CrossRefGoogle ScholarPubMed
Barrat, A., Barthélemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Batagelj, V. (1994). Semirings for social networks analysis. Journal of Mathematical Sociology, 1 (19), 53–68.CrossRefGoogle Scholar
Berger, C. R., & Burgoon, M. (1998). Communications and social influence processes. Lansing, MI: Michigan State University Press.Google Scholar
Breiger, R. L., Boorman, S. A., & Arabie, P. (1975). An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12 (3), 328–383.CrossRefGoogle Scholar
Burt, R. S. (1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92 (6), 1287–1335.CrossRefGoogle Scholar
Cartwright, D., & Harary, F. (1956). Structural balance: A generalization of heider's theory. Psychological Review, 63 (5), 277–293.CrossRefGoogle ScholarPubMed
Coleman, J. S., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study. Indianapolis, IN: Bobbs-Merrill Co.Google Scholar
Davis, J. A. (1967). Clustering and structural balance in graphs. Human Relations, 20 (2), 181–187.CrossRefGoogle Scholar
De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M., . . . Arenas, A. (2013). Mathematical formulation of multi-layer networks. Physical Review X, 3, 041022.CrossRefGoogle Scholar
de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with pajek. Structural Analysis in the Social Sciences, vol 34. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Doreian, P., & Mrvar, A. (2015). Structural balance and signed international relations. Journal of Social Structure, 2 (13), 1–49.CrossRefGoogle Scholar
Doreian, P., Batagelj, V., & Ferligoj, A. (2004). Generalized blockmodeling. Structural Analysis in the Social Sciences, vol. 25. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Estrada, E., & Benzi, M. (2014). Walk-based measure of balance in signed networks: Detecting lack of balance in social networks. Physical Review E, 90, 042802.CrossRefGoogle ScholarPubMed
Festinger, L. (1950). Informal social communication. Psychological Review, 57 (5), 271–82.CrossRefGoogle ScholarPubMed
Fienberg, S. E., & Wasserman, S. (1981). Categorical data analysis of single sociometric relations. Sociological Methodology, 12 (1), 156–192.CrossRefGoogle Scholar
Fienberg, S. E., Meyer, M. M., & Wasserman, S. S. (1985). Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80 (389), 51–67.CrossRefGoogle Scholar
Flament, C. (1963). Applications of graph theory to group structure. Prentice-Hall series in Mathematical Analysis of Social Behavior. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Friedkin, N. E. (1984). Structural cohesion and equivalence explanations of social homogeneity. Sociological Methods & Research, 12 (3), 235–261.CrossRefGoogle Scholar
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software–Practice & Experience, 21 (11), 1129–1164.CrossRefGoogle Scholar
Gallagher, H., & Robins, G. (2015). Network statistical models for language learning contexts: Exponential random graph models and willingness to communicate. Language Learning, 65 (4), 929–962.CrossRefGoogle Scholar
Gansner, E. R., Koren, Y., & North, S. (2005). Graph drawing by stress majorization. In Graph Drawing: 12th International Symposium, gd 2004, New York, NY, USA, September 29–October 2, 2004, revised selected papers. Berlin Heidelberg: Springer. pp. 239–250.CrossRefGoogle Scholar
Harary, F., Norman, Z., & Cartwright, D. (1965). Structural models: An introduction to the theory of directed graphs. New York, NY: John Wiley & Sons.Google Scholar
Heider, F. (2013). The psychology of interpersonal relations. Oxfordshire: Taylor & Francis.CrossRefGoogle Scholar
Holland, P. W., & Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs (with discussion). Journal of the American Statistical Association, 76 (373), 33–65.CrossRefGoogle Scholar
Homans, G. C. (1961). Social behavior: Its elementary forms. New York, NY: Harcourt Brace.Google Scholar
Kivelä, M., Arenas, A., Barthélemy, M., Gleeson, J., Moreno, Y., & Porter, M. (2014). Multilayer networks. Journal of Complex Networks, 2 (3), 203–271.CrossRefGoogle Scholar
Lazarsfeld, P., Berelson, B., & Gaudet, H. (1948). The people's choice: How the voter makes up his mind in presidential campaigns. New York, NY: Columbia University Press.Google Scholar
Lazega, E. (2001). The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford: Oxford University Press.CrossRefGoogle Scholar
Lazega, E., & Pattison, P. E. (1999). Multiplexity, generalized exchange and cooperation in organizations: A case study. Social Networks, 21 (1), 67–90.CrossRefGoogle Scholar
Leenders, R. T. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24 (1), 21–47.CrossRefGoogle Scholar
Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks. Journal of Mathematical Sociology, 1 (1), 49–80.CrossRefGoogle Scholar
Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Psychometrika, 2 (14), 95–116.CrossRefGoogle Scholar
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models for social networks: Theory, methods, and applications. Structural Analysis in the Social Sciences, vol. 35. Cambridge: Cambridge University Press.Google Scholar
McKnight, L. W., Vaaler, P. M., & Katz, R. L. (2002). Creative destruction: Business survival strategies in the global internet economy. Cambridge: MIT Press.Google Scholar
Ostoic, J. A. R. (2016a). multigraph: Plot and Manipulate Multigraphs. R package devel version 0.45.Google Scholar
Ostoic, J. A. R. (2016b). multiplex: Algebraic Tools for the Analysis of Multiple Social Networks. R package version 2.4.1.Google Scholar
Ostoic, J. A. R. (2013). Algebraic methods for the analysis of multiple social networks and actors attributes. Ph.D. thesis, University of Southern Denmark.Google Scholar
R Core Team. (2015). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.Google Scholar
Rogers, E. (2003). The diffusion of innovations. 5th edn. (1st edn. 1964). New York, NY: The Free Press.Google Scholar
Ryan, R., & Gross, N. (1943). The diffusion of hybrid seed corn in two iowa communities. Rural Sociology, 1 (8), 15–24.Google Scholar
Salehi, M., Sharma, R., Marzolla, M., Magnani, M., Siyari, P., & Montesi, D. (2015). Spreading processes in multilayer networks. IEEE Transactions on Network Science and Engineering, 2 (2), 65–83.CrossRefGoogle Scholar
Sampson, F. S. (1969). A novitiate in a period of change: An experimental and case study of social relationships. Ph.D. thesis, Cornell University.Google Scholar
Simmel, G., & Wolff, K. H. (1950). The sociology of Georg Simmel. New York, NY: Free Press.Google Scholar
Snijders, T. A. B., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006). New specifications for exponential random graph models. Sociological Methodology, 36 (99), 99–153.CrossRefGoogle Scholar
Strang, D., & Tuma, N. B. (1993). Spatial and temporal heterogeneity in diffusion. American Journal of Sociology, 3 (99), 614–639.CrossRefGoogle Scholar
Valente, T. W. (2010). Social networks and health. Oxford: Oxford University Press.CrossRefGoogle Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Structural Analysis in the Social Sciences, vol. 8. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Wellman, B. (1988). Structural analysis: From method and metaphor to theory and substance. Chap. 1, In Wellman, B., & Berkowitz, S. D. (Eds.), Social structures: A network approach (pp. 19–61). Cambridge: Cambridge University Press.Google Scholar
White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple networks I: Blockmodels of roles and positions. American Journal of Sociology, 81 (4), 730–780.CrossRefGoogle Scholar