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Beyond homophily: Incorporating actor variables in statistical network models

Published online by Cambridge University Press:  25 April 2019

Tom A.B. Snijders  and
Alessandro Lomi
Tom A.B. Snijders*
Affiliation:
Department of Sociology, University of Groningen, Grote Rozenstraat 31, 9712TG Groningen, The Netherlands and Nuffield College, University of Oxford, OX1 1NF Oxford, UK
Alessandro Lomi
Affiliation:
Institute of Computational Science, University of Italian Switzerland, 6900 Lugano, Switzerland and University of Exeter Business School, EX4 4PU Exeter, UK. (e-mail: alessandro.lomi@usi.ch)
*
*Corresponding author. Email: Tom.Snijders@nuffield.ox.ac.uk
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Abstract

We consider the specification of effects of numerical actor attributes, having an interval level of measurement, in statistical models for directed social networks. A fundamental mechanism is homophily or assortativity, where actors have a higher likelihood to be tied with others having similar values of the variable under study. But there are other mechanisms that may also play a role in how the attribute values of two actors influence the likelihood of a tie between them. We discuss three additional mechanisms: aspiration, the tendency to send more ties to others having high values; attachment conformity, sending more ties to others whose values are close to the “social norm”; and sociability, where those having higher values will tend to send more ties generally. These mechanisms may operate jointly, and then their effects will be confounded. We present a specification representing these effects simultaneously by a four-parameter quadratic function of the values of sender and receiver. Flexibility can be increased by a five-parameter extension. We argue that for numerical actor attributes having important effects on directed networks, these specifications may provide an improvement. An illustration is given of dependence of advice ties on academic grades, analyzed by the Stochastic Actor-oriented Model.

Keywords

actor covariatedirected networkshomophilyassortativityaspirationconformitysociabilitystochastic actor-oriented modelquadratic modelacademic performance

Information

Type
Original Article
Information
Network Science , Volume 7 , Issue 1 , March 2019 , pp. 1 - 19
Copyright
© Cambridge University Press 2019 

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References

Abrams, D., Wetherell, M., Cochrane, S., Hogg, M. A., & Turner, J. C. (1990). Knowing what to think by knowing who you are: Self-categorization and the nature of norm formation, conformity and group polarization. British Journal of Social Psychology, 29, 97–119.CrossRefGoogle ScholarPubMed
Azoulay, P., Liu, C., & Stuart, T. (2017). Social influence given (partially) deliberate matching: Career imprints in the creation of academic entrepreneurs. American Journal of Sociology, 122, 1223–1271.CrossRefGoogle Scholar
Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286, 509–512.CrossRefGoogle ScholarPubMed
Block, P. (2015). Reciprocity, transitivity, and the mysterious three-cycle. Social Networks, 40, 163–173.CrossRefGoogle Scholar
Block, P. (2018). Network evolution and social situations. Sociological Science, 5, 402–431.CrossRefGoogle Scholar
Block, P., & Grund, T. (2014). Multidimensional homophily in friendship networks. Network Science, 2, 189–212.CrossRefGoogle ScholarPubMed
Brouwer, J., Flache, A., Jansen, E., Hofman, A., & Steglich, C. (2018). Emergent achievement segregation in freshmen learning community networks. Higher Education, 76, 483–500.CrossRefGoogle Scholar
Cohen, J. M. (1977). Sources of peer group homogeneity. Sociology of Education, 50, 227–241.CrossRefGoogle Scholar
Coleman, J. S. (1961). The adolescent society. New York: The Free Press of Glencoe.Google Scholar
Coleman, J. S. (1966). Equality of educational opportunity [summary report], vol. 2. US Department of Health, Education, and Welfare, Office of Education.Google Scholar
Coombs, C. H. (1964). A theory of data. New York: Wiley.Google Scholar
de Solla Price, D. J. (1976). A general theory of bibliometric and other advantage processes. Journal of the American Society for Information Science, 27, 292–306.CrossRefGoogle Scholar
Fienberg, S., & Wasserman, S. (1981). Categorical data analysis of single sociometric relations. In Leinhardt, S. (Ed.), Sociological methodology (pp. 156–192). San Francisco: Jossey-Bass.Google Scholar
Goodreau, S. M. (2007). Advances in exponential random graph (p*) models applied to a large social network. Social Networks, 29, 231–248.CrossRefGoogle ScholarPubMed
Gremmen, M. C., Berger, C., Ryan, A. M., Steglich, C. E., Veenstra, R., & Dijkstra, J. K. (2018). Adolescents’ friendships, academic achievement, and risk behaviors: Same-behavior and cross-behavior selection and influence processes. Child Development (in Press) doi: 10.1111/cdev.13045.CrossRefGoogle Scholar
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97, 1090–1098.CrossRefGoogle Scholar
Homans, G. C. (1974). Elementary forms of social behavior. New York: Harcourt, Brace, Jovanovich.Google Scholar
Hunter, D. R. (2007). Curved exponential family models for social networks. Social Networks, 29, 216–230.CrossRefGoogle ScholarPubMed
Jones, L. E. (1983). Multidimensional models of social perception, cognition, and behavior. Applied Psychological Measurement, 7, 451–472.CrossRefGoogle Scholar
Kilduff, M., & Krackhardt, D. (1994). Bringing the individual back in: A structural analysis of the internal market for reputation in organizations. Academy of Management Journal, 37, 87–108.Google Scholar
Knudsen, T. (2008). Reference groups and variable risk strategies. Journal of Economic Behavior and Organization, 66, 22–36.CrossRefGoogle Scholar
Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as social process. In Berger, M., Abel, T., and Page, C. (Eds.), Freedom and control in modern society (pp. 18–66). New York: Van Nostrand.Google Scholar
Lewin, K., Dembo, T., Festinger, L., & Sears, P. S. (1944). Level of aspiration. In Hunt, J. (Ed.), Personality and the behavioral disorders (pp. 333–378). New York: Ronald Press.Google Scholar
Lomi, A., Snijders, T. A. B., Steglich, C., & Torló, V. J. (2011). Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Social Science Research, 40, 1506–1520.CrossRefGoogle ScholarPubMed
Lott, A. J., & Lott, B. E. (1965). Group cohesiveness as interpersonal attraction: A review of relationships with antecedent and consequent variables. Psychological Bulletin, 66, 395–415.Google Scholar
Louch, H. (2000). Personal network integration: transitivity and homophily in strong-tie relations. Social Networks, 22, 45–64.CrossRefGoogle Scholar
Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential random graph models. Cambridge: Cambridge University Press.Google Scholar
McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60, 175–215.CrossRefGoogle ScholarPubMed
McPherson, J. M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.CrossRefGoogle Scholar
McPherson, M. (2004). A Blau space primer: Prolegomenon to an ecology of affiliation. Industrial and Corporate Change, 13, 263–280.CrossRefGoogle Scholar
Mercken, L., Steglich, C. E. G., Sinclair, P., Holliday, J., & Moore, L. (2012). A longitudinal social network analysis of peer influence, peer selection, and smoking behavior among adolescents in British schools. Health Psychology, 31(4), 450–459.CrossRefGoogle ScholarPubMed
Osgood, D. W., Ragan, D. T., Wallace, L., Gest, S. D., Feinberg, M. E., & Moody, J. (2013). Peers and the emergence of alcohol use: Influence and selection processes in adolescent friendship networks. Journal of Research on Adolescence, 23, 500–512.CrossRefGoogle ScholarPubMed
Podolny, J. M. (1994). Market uncertainty and the social character of economic exchange. Administrative Science Quarterly, 39, 458–483.CrossRefGoogle Scholar
Ripley, R. M., Snijders, T. A. B., Bóda, Z., Vörös, A., & Preciado, P. (2018). Manual for Siena version 4.0. Technical report, Department of Statistics, Nuffield College, University of Oxford, Oxford.Google Scholar
Rivera, M. T., Soderstrom, S. B., & Uzzi, B. (2010). Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annual Review ofSociology, 36, 91–115.Google Scholar
Robins, G. (2009). Understanding individual behaviors within covert networks: The interplay of individual qualities, psychological predispositions, and network effects. Trends in Organized Crime, 12, 166–187.CrossRefGoogle Scholar
Robins, G., Elliott, P., & Pattison, P. (2001). Network models for social selection processes. Social Networks, 23, 1–30.CrossRefGoogle Scholar
Sauder, M., Lynn, F., & Podolny, J. M. (2012). Status: Insights from organizational sociology. Annual Review of Sociology, 38, 267–283.CrossRefGoogle Scholar
Sauerbrei, W., Royston, P., & Binder, H. (2007). Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Statistics in Medicine, 26, 5512–5528.CrossRefGoogle ScholarPubMed
Schaefer, D. R. (2018). A network analysis of factors leading adolescents to befriend substance-using peers. Journal of Quantitative Criminology, 34, 275–312.CrossRefGoogle Scholar
Selfhout-Van Zalk, M. H. W., Burk, W., Branje, S. J. T., Denissen, J., van Aken, M., & Meeus, W. H. J. (2010). Emerging late adolescent friendship networks and big five personality traits: A social network approach. Journal ofPersonality, 78, 509–538.CrossRefGoogle Scholar
Sherif, M. (1936). The psychology of social norms. Oxford: Harper.Google Scholar
Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361–395.CrossRefGoogle Scholar
Snijders, T. A. B. (2016). The multiple flavours of multilevel issues for networks. In Lazega, E., and Snijders, T. A. B. (Eds.), Multilevel network analysis for the social sciences; theory, methods and applications (pp. 15–46). Cham: Springer.CrossRefGoogle Scholar
Snijders, T. A. B. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and Its Application, 4, 343–363.CrossRefGoogle Scholar
Snijders, T. A. B., Lomi, A., & Torló, V. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265–276.CrossRefGoogle ScholarPubMed
Snijders, T. A. B., Pattison, P. E., Robins, G. L., & Handcock, M. S. (2006) New specifications for exponential random graph models. Sociological Methodology, 36, 99–153.CrossRefGoogle Scholar
Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. (2010). Introduction to actor-based models for network dynamics. Social Networks, 32, 44–60.CrossRefGoogle Scholar
Stokman, F. N. (2004). What binds us when with whom? Content and structure in social network analysis. Keynote Address at the SUNBELT XXIV International Network for Social Network Analysis Conference, May 13, 2004, in Portoroz (Slovenia).Google Scholar
Stokman, F. N., & Vieth, M. (2004). Was verbindet uns wann mit wem? Inhalt und Struktur in der Analyse sozialer Netzwerke. Kölner Zeitschrift für Soziologie und Sozialpsychologie, Sonderheft, 44, 274–302.Google Scholar
Stuart, T. E., Hoang, H. & Hybels, R. C. (1999). Interorganizational endorsements and the performance of entrepreneurial ventures. Administrative Science Quarterly, 44, 315–349.CrossRefGoogle Scholar
van Duijn, M. A., Snijders, T. A. B., & Zijlstra, B. H. (2004). p2: A random effects model with covariates for directed graphs. Statistica Neerlandica, 58, 234–254.CrossRefGoogle Scholar
Wasserman, L. (2004). All of statistics: A concise course in statistical inference. New York: Springer.CrossRefGoogle Scholar
Wasserman, S., & Pattison, P. (1996). Logit models and logistic regression for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61, 401–425.CrossRefGoogle Scholar