Hostname: page-component-669899f699-7tmb6 Total loading time: 0 Render date: 2025-04-25T21:32:47.204Z Has data issue: false hasContentIssue false
  • English
  • Français

Nodal Heterogeneity can Induce Ghost Triadic Effects in Relational Event Models

Published online by Cambridge University Press:  01 January 2025

Rūta Juozaitienė Open the ORCID record for Rūta Juozaitienė [Opens in a new window]  and
Ernst C. Wit
Rūta Juozaitienė*
Affiliation:
Vytautas Magnus University
Ernst C. Wit
Affiliation:
Università della Svizzera italiana
*
Correspondence should be made to Rūta Juozaitien˙e, Vytautas Magnus University, Kaunas, Lithuania. ruta.juozaitiene@vdu.lt
Get access Rights & Permissions [Opens in a new window]

Abstract

Temporal network data is often encoded as time-stamped interaction events between senders and receivers, such as co-authoring scientific articles or communication via email. A number of relational event frameworks have been proposed to address specific issues raised by complex temporal dependencies. These models attempt to quantify how individual behaviour, endogenous and exogenous factors, as well as interactions with other individuals modify the network dynamics over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects. The propensity to form or receive ties can also, at least partially, be related to actor attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates. However, comprehensively capturing all personality traits is difficult in practice, if not impossible. A failure to account for heterogeneity may confound the substantive effect of key variables of interest. This work shows that failing to account for node level sender and receiver effects can induce ghost triadic effects. We propose a random-effect extension of the relational event model to deal with these problems. We show that it is often effective over more traditional approaches, such as in-degree and out-degree statistics. These results that the violation of the hierarchy principle due to insufficient information about nodal heterogeneity can be resolved by including random effects in the relational event model as a standard.

Keywords

relational event modellingtriadic effectshierarchy principlerandom effectspopularityexpansiveness
Type
Theory & Methods
Information
Psychometrika , Volume 89 , Issue 1 , March 2024 , pp. 151 - 171
Copyright
Copyright © 2024 The Author(s), under exclusive licence to The Psychometric Society.

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.)

Article purchase

Temporarily unavailable

References

Artico, I, Wit, E.C.. (2023). Dynamic latent space relational event model. Journal of the Royal Statistical Society Series A: Statistics in Society, 186 3508–529.CrossRefGoogle Scholar
Back, M.D.. (2015). Opening the process black box: Mechanisms underlying the social consequences of personality. European Journal of Personality, 29, 91–96.CrossRefGoogle Scholar
Bianconi, G, Darst, R-K, Iacovacci, J, Fortunato, S. (2014). Triadic closure as a basic generating mechanism of communities in complex networks. Physical Review E, 90, 042806.CrossRefGoogle ScholarPubMed
Borgatti, S.P., Halgin, D.S.. (2011). On network theory. Organization Science, 22 51168–1181.CrossRefGoogle Scholar
Box-Steffensmeier, J.M., Campbell, B.W., Christenson, D, Morgan, J. (2019). Substantive implications of unobserved heterogeneity: Testing the frailty approach to exponential random graph models. Social Networks, 59, 141–153.CrossRefGoogle Scholar
Box-Steffensmeier, J.M., Christenson, D.P., Morgan, J.W.. (2018). Modeling unobserved heterogeneity in social networks with the frailty exponential random graph model. Political Analysis, 26 13–19.CrossRefGoogle Scholar
Butts, C-T. (2008). A relational event framework for social action. Sociological Methodology, 38 1155–200.CrossRefGoogle Scholar
Butts, C.T., Lomi, A, Snijders, T.A., Stadtfeld, C. (2023). Relational event models in network science. Network Science, 11 2175–183.CrossRefGoogle Scholar
Corbo, L, Corrado, R, Ferriani, S. (2016). A new order of things: Network mechanisms of field evolution in the aftermath of an exogenous shock. Organization Studies, 37 3323–348.CrossRefGoogle Scholar
DuBois, C., Butts, C., & Smyth P. (2013). Stochastic blockmodeling of relational event dynamics. In Artificial intelligence and statistics. PMLR (pp. 238–246).Google Scholar
Fischbacher, U, Gächter, S, Fehr, E. (2001). Are people conditionally cooperative? evidence from a public goods experiment. Economics Letters, 71 3397–404.CrossRefGoogle Scholar
Foster, D-V, Foster, J-G, Grassberger, P, Paczuski, M. (2011). Clustering drives assortativity and community structure in ensembles of networks. Physical Review E, 84, 066117.CrossRefGoogle ScholarPubMed
Gelman, A, Hill, JData analysis using regression and multilevel/hierarchical models 2006 CambridgeCambridge University Press.CrossRefGoogle Scholar
Geukes, K, Breil, S.M., Hutteman, R, Nestler, S, Küfner, A.C., Back, M.D.. (2019). Explaining the longitudinal interplay of personality and social relationships in the laboratory and in the field: The pils and the connect study. PloS one, 14 1.CrossRefGoogle ScholarPubMed
Hinde, R.A.Towards understanding relationships 1979 LondonPublished in cooperation with European Association of Experimental Social Psychology by Academic Press.Google Scholar
Isen, A.M.. (1987). Positive affect, cognitive processes, and social behavior. Advances in Experimental Social Psychology, 20, 203–253.CrossRefGoogle Scholar
Juozaitienė, R, Wit, E.C.. (2022). Non-parametric estimation of reciprocity and triadic effects in relational event networks. Social Networks, 68, 296–305.CrossRefGoogle Scholar
Kevork, S, Kauermann, G. (2021). Iterative estimation of mixed exponential random graph models with nodal random effects. Network Science, 9 4478–498.CrossRefGoogle Scholar
Klimek, P, Thurner, S. (2013). Triadic closure dynamics drives scaling-laws in social multiplex networks. New Journal of Physics, 15, 063008.CrossRefGoogle Scholar
Klimt, B., & Yang, Y. (2004). The enron corpus: A new dataset for email classification research. In European conference on machine learning (pp. 217–226).CrossRefGoogle Scholar
Kumpula, J.M., Onnela, J-P, Saramäki, J, Kaski, K, Kertész, J. (2007). Emergence of communities in weighted networks. Physical Review Letters, 99 22.CrossRefGoogle ScholarPubMed
Lerner, J, Bussmann, M, Snijders, T.A., Brandes, U. (2013). Modeling frequency and type of interaction in event networks. Corvinus Journal of Sociology and Social Policy, 4 13–32.CrossRefGoogle Scholar
Leskovec, J., Backstrom, L., Kumar, R., & Tomkins, A. (2008). Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’08, New York, NY, USA (pp. 462–470). ACM.CrossRefGoogle Scholar
Li, M, Zou, H, Guan, S, Gong, X, Li, K, Di, Z, Lai, C. (2013). A coevolving model based on preferential triadic closure for social media networks. Scientific Reports, 3, 2512.CrossRefGoogle ScholarPubMed
Lusher, D, Koskinen, J, Robins, GExponential random graph models for social networks: Theory, methods, and applications 2013 CambridgeCambridge University Press.Google Scholar
Madan, A, Cebrian, M, Moturu, S, Farrahi, K et.al Sensing the “health state” of a community IEEE Pervasive Computing 2011 11 436–45.CrossRefGoogle Scholar
Mcfarland, D. (2001). Student resistance: How the formal and informal organization of classrooms facilitate everyday forms of student defiance. American Journal of Sociology, 107, 612–678.CrossRefGoogle Scholar
McPherson, M, Smith-Lovin, L, Cook, J.M.. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27 1415–444.CrossRefGoogle Scholar
Michalski, R, Kajdanowicz, T, Bródka, P, Kazienko, P. (2014). Seed selection for spread of influence in social networks: Temporal vs. static approach. New Generation Computing, 32 3–4213–235.CrossRefGoogle Scholar
Newman, M-E-J, Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68, 036122.CrossRefGoogle ScholarPubMed
Olk, P.M., Gibbons, D.E.. (2010). Dynamics of friendship reciprocity among professional adults. Journal of Applied Social Psychology, 40 51146–1171.CrossRefGoogle Scholar
Perry, P, Wolfe, P. (2013). Point process modeling for directed interaction networks. Journal of the Royal Statistical Society, 75 5821–849.CrossRefGoogle Scholar
Pfeiffer, T, Rutte, C, Killingback, T, Taborsky, M, Bonhoeffer, S. (2005). Evolution of cooperation by generalized reciprocity. Proceedings of the Royal Society B: Biological Sciences, 272 15681115–1120.CrossRefGoogle ScholarPubMed
Pilny, A, Schecter, A, Poole, M.S., Contractor, N. (2016). An illustration of the relational event model to analyze group interaction processes. Group Dynamics: Theory, Research, and Practice, 20 3181–195.CrossRefGoogle Scholar
Raush, H.L.. (1965). Interaction sequences. Journal of Personality and Social Psychology, 2 4487.CrossRefGoogle ScholarPubMed
Robins, G, Pattison, P, Wang, P. (2009). Closure, connectivity and degree distributions: Exponential random graph (p*) models for directed social networks. Social Networks, 31 2105–117.CrossRefGoogle Scholar
Rutte, C, Taborsky, M. (2007). Generalized reciprocity in rats. PLoS Biology, 5 7.CrossRefGoogle ScholarPubMed
Sapiezynski, P, Stopczynski, A, Lassen, D.D., Lehmann, S. (2019). Interaction data from the Copenhagen networks study. Scientific Data, 6 1315.CrossRefGoogle ScholarPubMed
Snijders, T, van de Bunt, G, Steglich, C. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32, 44–60.CrossRefGoogle Scholar
Snijders, T.A.. (2017). Stochastic actor-oriented models for network dynamics. Annual Review of Statistics and Its Application, 4, 343–363.CrossRefGoogle Scholar
Stadtfeld, C, Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4 14318–352.CrossRefGoogle Scholar
Steele, F. (2003). A discrete-time multilevel mixture model for event history data with long-term survivors, with an application to an analysis of contraceptive sterilization in bangladesh. Lifetime Data Analysis, 9 2155–174.CrossRefGoogle Scholar
Therneau, T, Grambsch, PModeling survival data: Extending the Cox model 2000 New YorkSpringer.CrossRefGoogle Scholar
Thiemichen, S, Friel, N, Caimo, A, Kauermann, G. (2016). Bayesian exponential random graph models with nodal random effects. Social Networks, 46, 11–28.CrossRefGoogle Scholar
Uzaheta, A, Amati, V, Stadtfeld, C. (2023). Random effects in dynamic network actor models. Network Science, 11 2249–266.CrossRefGoogle Scholar
Vu, D, Lomi, A, Mascia, D, Pallotti, F. (2017). Relational event models for longitudinal network data with an application to interhospital patient transfers. Statistics in Medicine, 36 142265–2287.CrossRefGoogle ScholarPubMed
Yarmoshuk, A.N., Cole, D.C., Mwangu, M, Guantai, A.N., Zarowsky, C. (2020). Reciprocity in international interuniversity global health partnerships. Higher Education, 79 3395–414.CrossRefGoogle Scholar