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Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
Open accessAbstract
The authors consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual’s covariates on his or her behavior or other measurable responses. The authors show that generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular the authors demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual’s enduring traits and his or her choices, even when there is no intrinsic affinity between them. The authors also suggest some possible constructive responses to these results.
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“One solution to this problem is to model tie formation (and dissolution) explicitly (Shalizi & Thomas 2011). In the model of Figure 12, this can be accomplished by measuring and conditioning on the tie-forming characteristics of individual i, Ui, or individual j, Uj, or both.”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011
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“Shalizi & Thomas (2011) recently showed that latent homophily bias constitutes a previously unrecognized example of endogenous selection bias, where the social tie itself plays the role of the pre-treatment collider.”
From Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable (2014) · cites this paper as Shalizi & Thomas 2011
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“network studies of peer influence and social contagion suffer from a stubborn challenge of disambiguating such effects from homophily among peers and within communities (Christakis and Fowler, 2007; Lyons, 2011; Shalizi and Thomas, 2011).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Shalizi and Thomas, 2011
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Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567 · cited 3×
“Or rather are they close in the network because of these similarities, through the processes known assortative mixing on traits, or more simply as homophily (McPherson et al., 2001)?”
BackgroundInfluentialCommunity structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502 · cited 1×
“…inter-connected clusters of nodes, with minimal connection between clusters — has been thoroughly explored in the recent literature (explicitly in Girvan and Newman (2002); Newman and Girvan (2003); Bickel and Chen (2009); Porter et al. (2009); Fortunato (2010), implicitly in much smaller…”
BackgroundFinding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · cited 1×
“…of nodes, with minimal connection between clusters — has been thoroughly explored in the recent literature (explicitly in Girvan and Newman (2002); Newman and Girvan (2003); Bickel and Chen (2009); Porter et al. (2009); Fortunato (2010), implicitly in much smaller clusters in Elwert and…”
BackgroundCausality : Models , Reasoning , and Inference
Bentler, P., Heslenfeld, D., Molenaar, P., & Mcdonald, R. (2002) · Choice Reviews Online · doi:10.5860/choice.47-3771 · Cited by 13,039 · cited 1×
“The “easy” solution is to use expert knowledge of the system to identify all causally relevant variables, measure a sufficient set of them, and adjust for them appropriately (Morgan and Winship, 2007; Pearl, 2000; Spirtes et al., 2001).”
MethodCausality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293 · cited 1×
“…with minimal connection between clusters — has been thoroughly explored in the recent literature (explicitly in Girvan and Newman (2002); Newman and Girvan (2003); Bickel and Chen (2009); Porter et al. (2009); Fortunato (2010), implicitly in much smaller clusters in Elwert and Christakis (2008)).”
BackgroundREMEMBERING: A STUDY IN EXPERIMENTAL AND SOCIAL PSYCHOLOGY
Bartlett, F. C., & Burt, C. (1933) · British Journal of Educational Psychology · doi:10.1111/j.2044-8279.1933.tb02913.x · Cited by 7,475 · cited 1×
“…of actual processes of transmission and influence, which have been extensively studied in social psychology and allied fields since the 1920s (Bartlett, 1932; Sperber, 1996; Huckfeldt et al., 2004; Friedkin, 1998).13 However, not only is it adequate to demonstrate the existence of the…”
BackgroundIdentification of Endogenous Social Effects: The Reflection Problem
Manski, C. F. (1993) · The Review of Economic Studies · doi:10.2307/2298123 · Cited by 6,209
Causation, Prediction, and Search
Burr, T. (2003) · Technometrics · doi:10.1198/tech.2003.s776 · Cited by 6,185 · cited 7×
“19”
BackgroundMethodInfluential
Research works printed but not linked: 18
- Airoldi, Edoardo M., 2008, Journal of Machine Learning Research, 9, 1981
- Anagnostopoulos, Aris, KDD08: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Bakshy, Eytan, EC '09: Proceedings of the Tenth ACM Conference on Electronic Commerce
- Bourdieu, Pierre., 1984, Distinction:A Social Critique of the Judgement of Taste
- 10.1515/9781400840960 · DOI
- Gillespie, John H., 1998, Population Genetics: A Concise Guide
- 10.1093/oso/9780198546412.001.0001 · DOI · Paper page
- 10.1017/CBO9780511617102 · DOI
- Kipling, Rudyard., 1912, Just So Stories
- Leenders, Roger T. A. J., 1995, Structure and Influence: Statistical Models for the Dynamics of Actor Attributes, Network Structure and Their Interdependence
- 10.1007/978-1-4613-8542-4 · DOI
- 10.7208/chicago/9780226511993.001.0001 · DOI · Paper page
- McPherson, M., Paper presented at MERSIH
- Porter, Mason A., 2009, Notices of the American Mathematical Society, 56, 1164
- R Development Core Team., 2010, R: A Language and Environment for Statistical Computing
- Shalizi, Cosma Rohilla., AAAI 2008 Spring Symposia: Social Information Processing
- Siegfried, André., 1960, Germs and Ideas: Routes of Epidemics and Ideologies
- 10.7551/mitpress/1754.001.0001 · DOI · Paper page
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Items 501–600 of 894
Effect of Social Media Posts on Stock Market During COVID-19 Infodemic: An Agenda Diffusion Approach
Wang, X., & Vergeer, M. (2024) · SAGE Open · doi:10.1177/21582440241227688 · Cited by 9
“This inclination implies that information rooted in distinct professional disciplines tends to naturally group into network homophily (Shalizi & Thomas, 2011; A. Yang & Saffer, 2019).”
Graph-Based Methods for Discrete Choice
Tomlinson, K., & Benson, A. R. (2022) · Network Science · doi:10.1017/nws.2023.20 · Cited by 9
“Unfortunately, determining whether homophily or contagion is responsible for similar behavior among friends is notoriously difficult (and often impossible [51]).”
BackgroundHistorical and Comparative Research on Social Diffusion: Mechanisms, Methods, and Data
Everton, S. F., & Pfaff, S. J. (2022) · Social Science History · doi:10.1017/ssh.2021.46 · Cited by 12
SigGAN: Adversarial Model for Learning Signed Relationships in Networks
Chakraborty, R., Das, R., & Chandra, J. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532610 · Cited by 9
“Additionally, existing theories of social science, like homophily and social influence [21, 55], do not apply directly to signed networks.”
BackgroundChoices in networks: a research framework
Feinberg, F., Bruch, E., Braun, M., Falk, B., Fefferman, N., Feit, E. M., Helveston, J., Larremore, D. B., McShane, B., Patania, A., et al. (2020) · Marketing letters · doi:10.1007/s11002-020-09541-9 · Cited by 9
“…is notoriously difficult, due to intrinsic confounding of contagion — a network effect marketers and policy-makers seek to leverage — and homophily (Shalizi and Thomas 2011), as well as endogenous group formation, correlated unobservables, and simultaneity (Hartmann et al. 2008), the last often…”
BackgroundThe contagion of democracy through international networks
Cranmer, S. J., Desmarais, B. A., & Campbell, B. W. (2020) · Soc. Networks · doi:10.1016/j.socnet.2019.08.009 · Cited by 9
Information Diffusion in Social Networks: Friendship Paradox based Models and Statistical Inference
Krishnamurthy, V., & Nettasinghe, B. (2018) · Modeling, Stochastic Control, Optimization, and Applications · doi:10.48550/arxiv.1812.06790 · Cited by 9
“based diffusions are driven by influence of neighbors whereas homophily-based diffusions are driven by properties of nodes (which are correlated among neighbors) [3, 62, 83].”
BackgroundGender-specific Pathways of Peer Influence on Adolescent Suicidal Behaviors
Fletcher, J. M. (2017) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023117729952 · Cited by 9
Social influence on 5-year survival in a longitudinal chemotherapy ward co-presence network
Lienert, J., Marcum, C., Finney, J., Reed-Tsochas, F., & Koehly, L. K. (2017) · Network Science · doi:10.1017/nws.2017.16 · Cited by 9
“There are methodological issues when examining influence processes within such contexts— differentiating between selection or influence is generally difficult—limiting the types of inferences one can make (Shalizi & Thomas, 2012; VanderWeele, 2011).”
Networked Justice: Judges, the Diffusion of Ideas, and Legal Reform Movements in Mexico
Ingram, M. (2016) · Journal of Latin American Studies · doi:10.1017/s0022216x16001486 · Cited by 9
Comment on “Why and When 'Flawed' Social Network Analyses Still Yield Valid Tests of no Contagion”
Shalizi, C. (2012) · Statistics Politics and Policy · doi:10.1515/2151-7509.1053 · Cited by 9
“(That is why we used the time-delayed model in Shalizi and Thomas (2011).)”
MethodSelf-citationGroup Membership and Diffusion in Virtual Worlds
Huffaker, D. A., Teng, C., Simmons, M., Gong, L., & Adamic, L. A. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.218 · Cited by 9
“This is not unique to offline settings; a study of social influence in Wikipedia shows that users become rapidly similar after their first online communications, and this keeps increasing over time, making it difficult to tease social influence and homophily apart [17].”
BackgroundHoiles, W., Krishnamurthy, V., & Aprem, A. (2016) · IEEE Access · doi:10.1109/access.2016.2629478 · Cited by 9
Social Networking in Online and Offline Contexts
Levesque, R., Mikami, A., & Szwedo, D. E. (2011) · Springer eBooks · doi:10.1007/978-3-319-32132-5_283-2 · Cited by 9
TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks
Liu, Y., & Shen, Y. (2024) · arXiv.org · doi:10.48550/arxiv.2407.08064 · Cited by 8
“However, the correlation between feature and graph information is well known in literature [56, 57], overlooking the impact will lead to bad performance.”
BackgroundFood Choice Mimicry on a Large University Campus
Gligorić, K., Chiolero, A., Kıcıman, E., White, R., Horvitz, E., & West, R. (2023) · arXiv.org · doi:10.48550/arxiv.2308.16095 · Cited by 8
“The setting is informed by standard assumptions made to identify the causal effect of social influence under the presence of homophily in a pairwise setup, when examining the causes behind why a person manifested a behavior at a given time [50, 77].”
BackgroundLong-Range Social Influence in Phone Communication Networks on Offline Adoption Decisions
Leng, Y., Dong, X.-W., Egido, E. M., & Pentland, A. (2023) · Information systems research · doi:10.1287/isre.2023.1231 · Cited by 8
Distinguishing Homophily from Peer Influence Through Network Representation Learning
Chen, X., Liu, Y., & Zhang, C. (2022) · INFORMS journal on computing · doi:10.1287/ijoc.2022.1171 · Cited by 8
Causal Relationships between Personal Networks and Health: A Comparison of Three Modeling Strategies
Ruppel, E. H., Child, S. T., Fischer, C., & Botchway, M. (2022) · Journal of Health and Social Behavior · doi:10.1177/00221465211072310 · Cited by 8
“Second, in using egocentric network data, we do not address some issues specific to causal inference in network analysis, such as homophily and social contagion in sociocentric network data (Shalizi and Thomas 2011), the emphasis of much research by Christakis and Fowler (2007, 2008, 2013).”
MethodDetecting contagious spreading of urban innovations on the global city network
Kitzmann, N. H., Romańczuk, P., Wunderling, N., & Donges, J. F. (2022) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-022-00470-4 · Cited by 12
“In the presence of confounding effects such as homophily and shared local or global influences, contagion can be hard to identify [63].”
BackgroundSmall, M. L. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.042 · Cited by 10
How does homophily shape the topology of a dynamic network?
Li, X., Mobilia, M., Rucklidge, A. M., & Zia, R. K. P. (2021) · Physical review. E · doi:10.1103/physreve.104.044311 · Cited by 10
Lorenz, G., Boda, Z., & Salikutluk, Z. (2021) · Journal of Ethnic and Migration Studies · doi:10.1080/1369183x.2021.1898354 · Cited by 8
“Rather, secondary forms of homophily can reinforce network homogeneity if different forms of homophily are intertwined (Shalizi and Thomas 2011).”
BackgroundSusceptible individuals drive active social contagion
Chung, N., Chew, L., Chen, W., D'Souza, R., & Lai, C. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033125 · Cited by 8
Phithakkitnukoon, S., & Smoreda, Z. (2016) · Social Network Analysis and Mining · doi:10.1007/s13278-016-0351-z · Cited by 8
“In fact, homophily effect and social influence are frequently confounded and difficult to separate in non-experimental studies (Shalizi and Thomas 2011).”
BackgroundThe ‘ripple effect’ of schedule control: a social network approach
McAlpine, K. L. (2015) · Community Work & Family · doi:10.1080/13668803.2015.1080662 · Cited by 8
Lee, S., & Honavar, V. G. (2015) · ACI@UAI · doi:10.48550/arxiv.1508.02103 · Cited by 8
The Role of Peer Influence in Churn in Wireless Networks
Han, Q.-W., & Ferreira, P. A. (2014) · IEEE International Conference on Social Computing · doi:10.1145/2639968.2640057 · Cited by 8
“However, PSM still fails to provide full causal interpretations because it does not control for unobserved effects [29].”
BackgroundHitting Is Contagious in Baseball: Evidence from Long Hitting Streaks
Bock, J. R., Maewal, A., & Gough, D. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051367 · Cited by 8
“This relates to the dilemma of discerning homophily from influence [20]; better hitters might already have coalesced onto certain teams, and the contagious hitting is due to this structural grouping as opposed to a diffusive effect.”
BackgroundCommunity Detection in Social Networks Considering Social Behaviors
Wang, Y., Jin, D., He, D., Musiał, K., & Dang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3209704 · Cited by 12
“Since a community is a set of users who share similar topics [29], users’ interests in multiple topics result in multiple topic distributions in communities.”
BackgroundRewley, J., Fawzi, M. C. S., McAdam, K., Kaaya, S., Liu, Y., Todd, J., Andrew, I., & Onnela, J. P. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-033759 · Cited by 7
Simple contagion drives population-scale platform migrationPreprint
Quelle, D., Denker, F., Garg, P., & Bovet, A. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.24801 · Cited by 7
“Disentangling homophily from social contagion is difficult because similar people select into shared networks and encounter common environments and shocks, which align adoption times and produce clustering that is observationally equivalent to peer influence 8,9,44,45 .”
Blum, R., & Noel, H. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009495615 · Cited by 9
Vaccination homophily in ego contact networks during the COVID-19 pandemic
Stefkovics, Á., Albert, F., Ligeti, A., Dávid, B., Rudas, S., & Koltai, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-65986-2 · Cited by 7
Letina, S., Long, E., McCrorie, P., Mitchell, K. R., Zucca, C., Riddell, J., Simpson, S. A., Moore, L., & McCann, M. (2024) · Social Networks · doi:10.1016/j.socnet.2023.12.002 · Cited by 12
Partisanship and Political Socialization in Electoral Autocracies
Letsa, N. (2024) · American Political Science Review · doi:10.1017/s0003055424000261 · Cited by 7
Peer and Social Correlates of Smoking among Saudi Youth
Alqahtani, R. T., Moody, J., Alhemodi, N. N., Alghamdi, M. S., & Alhajhussein, S. (2024) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231241286735 · Cited by 7
“Determining the balance of these two forces toward similarity is difficult because the behaviors are generally endogenous (Shalizi and Thomas 2011), with each causing the other.”
Young, G. J., Zepeda, E., Flaherty, S., & Hasan, M. (2023) · Production and operations management · doi:10.1111/poms.14074 · Cited by 7
“The possible confounding of peer effects with social homophily has been noted in the literature (Shalizi & Thomas, 2011).”
BackgroundAssociation Between Surgical Opioid Prescriptions and Opioid Initiation by Opioid-naïve Spouses
Varady, N. H., Chen, A. F., Niu, R.-J., Chung, M., Freccero, D., & Smith, E. L. (2021) · Annals of Surgery · doi:10.1097/sla.0000000000005350 · Cited by 7
Does Whom Patients Sit Next to during Hemodialysis Affect Whether They Request a Living Donation?
Gillespie, A., Fink, E. L., Gardiner, H., Gadegbeku, C., Reese, P., & Obradovic, Z. (2021) · Kidney360 · doi:10.34067/kid.0006682020 · Cited by 7
“We then examined the social network theories of sociality (some people are more social than others, Table 1) (17), health-behavior homophily, and social contagion that could explain the increase in network participants who discussed living donation with other patients and requested a living donation from family and fr…”
BackgroundInfluentialAbstentions and Social Networks in Congress
Battaglini, M., Sciabolazza, V. L., & Patacchini, E. (2020) · Journal of Politics · doi:10.1086/723814 · Cited by 7
Statistical methods for the estimation of contagion effects in human disease and health networks
Xu, R. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.027 · Cited by 7
“As long as all of the variables affecting the behavioral outcome or both the behavior and selection are observed and controlled using this relatively simple model, contagion effects can be correctly estimated [16,20].”
BackgroundGift Contagion in Online Groups: Evidence from Virtual Red Packets
Yuan, Y., Liu, T. X., Tan, C., Chen, Q., Pentland, A., & Tang, J. (2023) · Management Science · doi:10.1287/mnsc.2023.4906 · Cited by 11
Sandberg, J., Park, C., Rytina, S., Delaunay, V., Douillot, L., Boujija, Y., Gning, S. B., Bignami, S., Sokhna, C., Belaid, L., et al. (2019) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2019.02.028 · Cited by 7
Identification of Causal Diffusion Effects Under Structural Stationarity.Preprint
Egami, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07858 · Cited by 7
“As shown in Shalizi and Thomas (2011), W is always, often implicitly, adjusted for in any causal diffusion analysis because researchers need to compare observations with similar spatial/network pre-treatment characteristics.”
BackgroundInfluentialChan, T., Yen, T.-J., Hu, T., Fu, Y.-C., & Hwang, J. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-020600 · Cited by 7
“Thus, it would be particularly difficult to determine and infer from observational data how personal moods spread or diffuse.(12) Even without claiming the causal effects that are essential for explaining spread or diffusion, however, it would be revealing to examine whether and how similar patterns of concurrent mood…”
BackgroundProduct diffusion through on-demand information-seeking behaviour
Riedl, C., Bjelland, J., Canright, G., Iqbal, A. M., Engø-Monsen, K., Qureshi, T., Sundsøy, P., & Lazer, D. (2018) · Journal of the Royal Society Interface · doi:10.1098/rsif.2017.0751 · Cited by 7
“This allows us to gain better understanding of human behaviour related to diffusion and contrasts with studies driven by observational data, in which confounding effects can always be present [36].”
Beautiful and Damned. Combined Effect of Content Quality and Social Ties on User Engagement
Aiello, L. M., Schifanella, R., Реди, М., Svetlichnaya, S., Liu, F., & Osindero, S. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2747552 · Cited by 12
“That is a much harder task [4, 61] because the benefit of randomization is lost, as the set of individuals who received the treatment is often pre-determined.”
BackgroundHeijmans, N., van Lieshout, J., & Wensing, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185341 · Cited by 7
“Noting that it is difficult to distinguish selection and causal effects [20] clustering seems to occur together with homophily.”
BackgroundThe coevolution of networks and health
Schaefer, D. R., & Adams, J. (2017) · Network Science · doi:10.1017/nws.2017.24 · Cited by 7
“Although influence is the predominate focus of health studies, inferences of network influence are frequently overstated (Shalizi & Thomas, 2011).”
BackgroundDynamics of self-control in egocentric social networks
vanDellen, M. R., Meisel, M. K., & Lynch, B. P. (2017) · Personality and Individual Differences · doi:10.1016/j.paid.2016.11.010 · Cited by 7
Who Are Like-Minded: Mining User Interest Similarity in Online Social Networks
Yang, C.-F., Zhou, Y., & Chiu, D.-M. W. (2016) · International Conference on Web and Social Media · doi:10.48550/arxiv.1603.02175 · Cited by 7
Follow My Friends This Friday! An Analysis of Human-Generated Friendship Recommendations
Gavilanes, R. G., O'Hare, N., Aiello, L., & Jaimes, A. (2013) · Social Informatics · doi:10.1007/978-3-319-03260-3_5 · Cited by 7
Analyzing the impact of events in an online music community
Tirado, J. M., Higuero, D., Isaila, F., & Carretero, J. (2011) · Workshop on Social Network Systems · doi:10.1145/1989656.1989662 · Cited by 7
Mubayi, A. (2017) · Handbook of statistics · doi:10.1016/bs.host.2017.08.003 · Cited by 7
Peer influence in social network: a review of identification, modeling and application
Wu, Z.-B., Li, Y., & Pedrycz, W. (2026) · Journal of business research · doi:10.1016/j.jbusres.2025.115806 · Cited by 6
Micro-Macro Mediation Analysis in Social Networks
Duxbury, S. W. (2024) · Sociological methodology · doi:10.1177/00811750231220950 · Cited by 6
“Because these possibilities are well studied and occur routinely in network analysis, not just mediation analysis (An 2015a; An et al. 2022; Centola 2010; Shalizi and Thomas 2011; Steglich et al. 2010), I do not belabor the point here.”
Social Alignment Contagion in Online Social Networks
Mirlohi, A., Mahdavimoghaddam, J., Jovanović, J., Al-Obeidat, F. N., Khani, M., Ghorbani, A. A., & Bagheri, E. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3226346 · Cited by 8
“To this end, observational techniques [16], [23] and randomized controlled trial (RCT) methods [24], [25] have been proposed in the literature.”
MethodThe Ties that Bind: War Histories and Online Social Networks in Postwar Societies
Lesschaeve, C., & Glaurdić, J. (2023) · Journal of Conflict Resolution · doi:10.1177/00220027231190917 · Cited by 9
“Two mechanisms, identi fi ed in earlier research albeit using different terminology (see Shalizi and Thomas 2011) are suggested to be driving this pattern.”
Generational effects of culture and digital media in former Soviet Republics
Horne, B., Rice, N., Luther, C. A., Ruck, D. J., Borycz, J., Allard, S., FitzGerald, M., Manaev, O., Prins, B. C., Taylor, M., et al. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01670-6 · Cited by 6
“Like with like” or “do like?” Modeling peer effects in the classroom
Pratschke, J., & Abbiati, G. (2023) · Social Science Quarterly · doi:10.1111/ssqu.13257 · Cited by 6
“In order to deal with this situation, as Shalizi and Thomas (2011) and VanderWeele (2011) show, it is possible to use a sensitivity testing approach to assess the robustness 7 The term 'spatial' is applied to all variables which have been weighted by W, regardless of whether this is a contiguity…”
BackgroundInfluentialLee, C., Chen, T.-W., Yu, Y.-F., Strong, C., Lin, C., Chang, Y.-H., Hsieh, Y., Lin, Y.-C., Herrera, J. J. U., & Tsai, M. (2022) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-022-00904-z · Cited by 7
“Thus, a causal effect of peer influence should be inferred with caution, although the SAOM has been used to estimate peer influence and friend selection simultaneously (Shalizi & Thomas, 2011).”
Zheng, B., Fletcher, J. M., Zheng, F., & Lu, Q. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.115249 · Cited by 10
Zhang, S., Xiao, T., & He, J. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.861743 · Cited by 7
“Those with a higher weight often experience more pressure to be slim and have been identified as a unique cluster, based on homophily and contagion assumptions, in social network studies of obesity (79).”
BackgroundEstimating Social Influence from Observational Data
Sridhar, D., Bacco, C. D., & Blei, D. (2022) · CLEaR · doi:10.48550/arxiv.2204.01633 · Cited by 6
“However, Shalizi and Thomas (2011) caution that is usually difficult to observe all confounders; unobserved factors that drive connections and purchases can still bias our estimates of social influence.”
BackgroundPrivate Benefits, Public Vices: Railways and Logrolling in the Nineteenth-Century British Parliament
Esteves, R., & Mesevage, G. G. (2021) · Journal Economic History · doi:10.1017/s0022050721000450 · Cited by 6
Strength of Weak Ties in the Labor Market: An Assessment of the State of Research
Fernandez, R. M. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.019 · Cited by 6
Wang, Y., Jin, D., Yang, C., & Dang, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00070 · Cited by 6
“Due to group homophily in networks [8], individuals in a community share similar topic interests, and they generate intracommunity links.”
BackgroundSpatiotemporal network structure among “friends of friends” reveals contagious disease process
Witte, C., Hungerford, L., Rideout, B., Papendick, R., & Fowler, J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237168 · Cited by 6
Lu, J., Xie, X., Lan, J., Peng, T.-Q., Wu, Y.-C., & Chen, W. (2019) · Visual Informatics · doi:10.1016/j.visinf.2019.06.002 · Cited by 6
Effect of Friends’ Churn on Consumer Behavior in Mobile Networks
Ferreira, P., Telang, R., & de Matos, M. G. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2019.1598683 · Cited by 6
“It is well known that empirical identification on whether the behavior of friends affects one’s behavior is hard to obtain with just observational data because homophily and reverse causality hinder our ability to do so [41, 54, 24].”
Probabilistic Causal Analysis of Social Influence
Bonchi, F., Gullo, F., Mishra, B., & Ramazzotti, D. (2018) · International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271756 · Cited by 6
“, assortative mixing [39] or “homophily”), unobserved influence, and transitivity of influence [46].”
BackgroundInferring models of opinion dynamics from aggregated jury data
Burghardt, K., Rand, W. M., & Girvan, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218312 · Cited by 6
Yuan, X., & Crooks, A. (2017) · International Conference on Social, Cultural, and Behavioral Modeling · doi:10.1007/978-3-319-60240-0_14 · Cited by 6
Linguistic Markers of Influence in Informal Interactions
Prabhumoye, S., Choudhary, S., Spiliopoulou, E., Bogart, C., Rosé, C., & Black, A. (2017) · NLP+CSS@ACL · doi:10.18653/v1/w17-2908 · Cited by 6
“The Homophily Confound: Shalizi and Thomas (2011) identify three factors that affect the activities in a social network: ‘Homophily’, ‘Social Influence’ and ‘Co-Variate Causation’.”
BackgroundStochastic actor-oriented modeling for studying homophily and social influence in OSS projects
Kavaler, D., & Filkov, V. (2017) · Empirical Software Engineering · doi:10.1007/s10664-016-9431-y · Cited by 6
“We illustrate this approach on case studies of two Apache Software Foundation (ASF) projects: Ant and Axis2/Java, on which we model three individual behaviors: one’s commit count, high file ownership, and minor contributor count (or low file ownership), all over time.1 We found that: 1.”
BackgroundMethodOn the Directionality Test of Peer Effects in Social Networks
An, W.-H. (2016) · Sociological Methods & Research · doi:10.1177/0049124115582271 · Cited by 6
Influencing Busy People in a Social Network
Sarkar, K., & Sundaram, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162014 · Cited by 6
Bhattacharya, P., Phan, T., Bai, X., & Airoldi, E. (2015) · International Conference on Interaction Sciences · doi:10.48550/arxiv.1512.06469 · Cited by 6
“…this puzzle amounts to separating the effect of social influence (i.e. when network influences attitude/behavior) from homophilous interactions (i.e. when attitude/behavior influences network formation) and context effects (Borgatti and Foster 2003; McPherson et al. 2001; Shalizi and Thomas 2011).”
BackgroundUnbiased metrics of friends’ influence in multi-level networks
Vidmer, A., Medo, M., & Zhang, Y.-C. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0057-x · Cited by 6
Neighborhood Effects in a Behavioral Randomized Controlled Trial
Pruitt, S., Leonard, T., Murdoch, J., Hughes, A., McQueen, A., & Gupta, S. (2014) · Health and Place · doi:10.1016/j.healthplace.2014.10.001 · Cited by 6
“…significant rho in the neighborhood effects literature – in health behaviors is small (e.g. Christakis and Fowler, 2007; Eisenberg and Quinn, 2006) and largely debated (e.g. Lyons, 2011; Cohen-Cole and Fletcher, 2008a, 2008b; Noel and Nyhan, 2011; Sainsbury, 2008; Shalizi and Thomas, 2011).”
New Problems, New Publics? Dewey and New Media: New Problems, New Publics? Dewey and New Media
Farrell, H. (2014) · Policy & Internet · doi:10.1002/1944-2866.poi363 · Cited by 6
Alcohol use among adolescents as a coordination problem in a dynamic network
Corten, R., & Knecht, A. (2013) · Rationality and Society · doi:10.1177/1043463112473793 · Cited by 6
“More recently, a debate has flared up on the identifiability of influence effects in networks (Shalizi and Thomas, 2011; VanderWeele, 2011), triggered in part by the remarkable findings of Christakis and Fowler (2007) and Fowler and Christakis (2008) on the contagion of obesity, happiness, and other traits.”
BackgroundIs Dispositional Happiness Contagious?
Matteson, L. K., McGue, M. K., & Iacono, W. G. (2013) · Journal of Individual Differences · doi:10.1027/1614-0001/a000103 · Cited by 11
“Whereas we used a straightforward behavioral genetic design, they performed social network research, critiques of which are now emerging in the literature (Lyons, 2011; Shalizi & Thomas, 2011).”
MethodComplex Contagion in Social Networks: Causal Evidence from a Country-Scale Field Experiment
Lee, J., Lazer, D. M. J., & Riedl, C. (2025) · Sociological Science · doi:10.15195/v12.a28 · Cited by 6
“Despite their central importance to social science, causal peer effects are notoriously difficult to estimate reliably in empirical data (Aral 2011; Manski 2000; Shalizi and Thomas 2011).”
Identifying Multiple Peer Influences on Smart Contract Adoption in Blockchain User NetworkPreprint
Cai, X.-D., Zhao, X., Zhang, B., & Feng, G. (2019) · Social Science Research Network · doi:10.2139/ssrn.3387794 · Cited by 6
A Manual for Cultural Analysis
Matthews, L. J., Brown, R., & Kennedy, D. P. (2018) · RAND Corporation eBooks · doi:10.7249/tl275 · Cited by 6
Social Cybersecurity: Reshaping Security Through An Empirical Understanding of Human Social Behavior
Das, S. (2017) · doi:10.1184/r1/6722918.v1 · Cited by 6
“Furthermore, regression analysis confounds homophily-based diffusion with social-influence based diffusion [6,92].”
BackgroundPorter, M. A., & Gleeson, J. P. (2016) · Frontiers in applied dynamical systems: reviews and tutorials · doi:10.1007/978-3-319-26641-1_3 · Cited by 7
A Primer on Deep Learning for Causal Inference
Koch, B., Sainburg, T., Geraldo, P., Jiang, S., Sun, Y., & Foster, J. G. (2024) · Sociological Methods & Research · doi:10.1177/00491241241234866 · Cited by 17
Bardach, L., Boda, Z., Neuendorf, C., Wagner, W., Davison, K. T., & Trautwein, U. (2024) · Trends in Neuroscience and Education · doi:10.1016/j.tine.2024.100224 · Cited by 8
“In addition to primary selection effects (friendships develop among students with similar perceptions of teaching quality), so-called secondary selection effects may occur as students ’ homophily is not one-dimensional and also operates based on other attributes (see also e.g., [7,36]).”
Measuring the communicative constitution of organization as network formation
Schwing, K. M., & Pitt, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300399 · Cited by 8
“Homophily must also be considered because it is often conflated with, and empirically very difficult to separate from, contagion [51, 52].”
Gao, Y., Campajola, C., Vallarano, N., Teixeira, A. S., & Tessone, C. (2024) · arXiv.org · doi:10.48550/arxiv.2401.12633 · Cited by 5
Kim, K., & Lee, S. K. (2023) · International Journal of Business Communication · doi:10.1177/23294884231217084 · Cited by 5
“Specifically, “homophily,” which is the tendency of individual actors with similar attributes to form ties with one another (Kossinets & Watts, 2009; Shalizi & Thomas, 2011), has been regarded as an important mechanism of network formation.”
Cross, J., Greene, D., Umansky, N., & Calò, S. (2023) · Journal of European Public Policy · doi:10.1080/13501763.2023.2242891 · Cited by 5
“It is thought to be an important aspect of many social behaviours and phenomena, including group in fl uence, contagion, and information di ff usion (Aral et al ., 2009; Bakshy et al ., 2012; Shalizi & Thomas, 2011).”
Buchanan, A. L., Katenka, N., Lee, Y., Wu, J. H., Pantavou, K. G., Friedman, S. R., Halloran, M. E., Marshall, B. D. L., Forastiere, L., & Nikolopoulos, G. K. (2023) · Pathogens · doi:10.3390/pathogens12020326 · Cited by 7
“The use of a potential outcomes framework provides an approach to deal with well-understood biases, such as confounding and selection bias, and address complex dependencies in outcomes due to the network structure [56–58], as well as biases specific to network designs, including homophily [60] and network dependence […”
BackgroundNetwork diffusion of gender diversity on boards: A process of two-speed opposing forces
Gimeno, R., de Cabo, R. M., Grau, P., & Gabaldón, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277214 · Cited by 5
“To this extent, we introduce into the diffusion framework, the fact that not only network structure matters, but also the way in which agents influence each other [33].”
BackgroundRelational Causal Models with Cycles: Representation and Reasoning
Ahsan, R., Arbour, D. T., & Zheleva, E. (2022) · CLEaR · doi:10.48550/arxiv.2202.10706 · Cited by 5
“However, Spirtes (1995); Neal (2000) show that without any specific assumption regarding the nature of dependence (i.”
BackgroundGeorg Simmel’s Contribution to Social Network Research
Hollstein, B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.003 · Cited by 13
On J. Clyde Mitchell’s “The Concept and Use of Social Networks”
Borgatti, S. P., & Halgin, D. S. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.007 · Cited by 4
Metadata
- Authors
- Shalizi, C. R., & Thomas, A. C.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Shalizi, Cosma Rohilla
- Venue
- Sociological Methods & Research
- Publisher
- SAGE Publications
- Published
- 2011-05
- Volume
- 40
- Issue
- 2
- Pages
- 211-239
- ISSN
- 0049-1241 (ISSN-L)
- 1552-8294
- PMC
- PMC3328971
- OpenAlex
- W2149084727
- Cited by
- 994
- References
- 63
- Citations by year
- 2026: 32 · 2025: 41 · 2024: 46 · 2023: 57 · 2022: 63 · 2021: 122 · 2020: 66 · 2019: 76 · 2018: 61 · 2017: 76 · 2016: 75 · 2015: 67 · 2014: 66 · 2013: 79 · 2012: 42
- FWCI
- 50.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Sociological Methods & Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS047493)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- homophily, social contagion, social influence, confounding, social networks, causal inference, imitation model, observational studies
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