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REVIEW 4 major objections 5 minor 118 references

Engineering Social Networks: How Initial Group Assignment Shapes Student Social Interactions

T0 review · 4 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Social-group assignment shapes student networks nearly entirely through triadic closure rather than direct link formation.

desk verdict A credible dyadic-effects paper wrapped around a fragile SUGM decomposition; the headline triadic-closure claim needs much more support. read the letter →

arxiv 2607.17926 v1 pith:4PZJMXYT submitted 2026-07-20 econ.EM

Johanna Einsiedler , Nikolaj Arpe Harmon , David Dreyer Lassen , Andreas Bjerre-Nielsen This is my paper · ORCID
classification econ.EM MSC 62P2091D30
keywords networkformationtriadicclosurerandomassignmentsubgraphgeneratedmodelsocialgroupsfriendshipnetworksinstrumentalvariableshighereducation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Putting first-year students into small social groups changes their friendship networks, and this paper argues that the change happens almost exclusively through triadic closure: a shared group makes friends of friends become friends, rather than directly connecting pairs. Using a randomized assignment of students to classrooms and to seven-person social groups, combined with smartphone-based measures of calls, texts, co-location, and Facebook ties, the authors estimate a network-formation model that separates direct pairwise link formation from triangle formation. For social groups, the effect on triangle formation is large and significant—each additional same-group pair in a triad raises the probability of closing the triangle by 3.7 percentage points—while the direct pairwise effect is small, negative, and insignificant. The 32-percentage-point total effect of social-group membership on the composite link measure is therefore attributed to incidental links that arise only when a triangle closes. Accounting for this changes policy conclusions: a dyadic model matches overall link counts but badly under-predicts clustering, and understates welfare gains that come through mutual-friend spillovers.

What carries the argument

Subgraph Generated Model (SUGM): network formation is modeled as two independent first-step processes—direct dyadic links and triadic triangles—whose union is the observed network. A control function based on the recentered random-assignment instrument (the deviation of same-group assignment from its pair-specific baseline probability) purges the endogeneity of realized group membership, and nonlinear least squares jointly estimates beta_1 (the direct link effect) and theta_1 (the effect of one additional same-group pair on triangle formation).

What would settle it

Re-estimate the SUGM adding study-program-level common shocks to the triangle equation; if the per-pair triangle coefficient theta_1 drops materially when program-wide shocks are included, the conditional-independence assumption behind the decomposition is violated and the triadic channel is over-stated.

Watch

Extended reading notes

Core claim

Using a subgraph-generated model estimated with the randomized group assignment as an instrument, the paper claims that being placed in a seven-person social group raises the probability that a triad of students becomes three mutually linked friends by 3.7 percentage points for each pair within the triad that shares the group (0.037, SE 0.007), while the direct pairwise-link effect of sharing the group is -0.027 (SE 0.110), statistically indistinguishable from zero. Because the observed composite network is the union of direct links and triangle links, the paper concludes that essentially all of the 32-percentage-point total effect of social-group placement on link formation is attributable

Load-bearing premise

The split between direct links and triangle-based links depends on there being no unmeasured common factor—like a study program's social atmosphere—that both pushes triads to close and pushes their members into the same group beyond the random assignment; if such a factor exists, the triadic coefficient absorbs it and the decomposition misattributes the group effect.

Editorial extensions

If this is right

  • If social groups operate mainly through triadic closure, assignments to small groups create dense local clusters rather than diffuse ties; policy should target clustering benefits, not just link counts.
  • Dyadic instrumental-variable estimates that ignore triangles will misattribute the group effect to direct pairwise attraction and will misstate the higher-order structure of the counterfactual network.
  • The same estimated parameters imply that classroom and social-group policies have qualitatively different network consequences, with classrooms (if anything) producing direct ties and social groups producing triangles.
  • When welfare depends on spillovers through mutual friends, model choice matters: the SUGM predicts higher average utility than the dyadic model once spillovers are nontrivial, so cost-benefit analyses of group assignment should embed closure.
  • Effects from the first semester persist but decline in later semesters, so group-induced triadic structure is a lasting but decaying feature of the network.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension would test the same decomposition in non-university settings (dorm rooms, workplace teams) to see whether triadic closure is the dominant channel whenever groups are small and socially oriented.
  • If the direct-link effect is truly near zero for seven-person groups, then interventions that merely introduce pairs (e.g., matching apps) may be poor substitutes for group-based settings that supply common friends.
  • A testable prediction follows from the welfare simulations: group-assignment effects on downstream outcomes like retention, grades, or referral-based job finding should be mediated by clustering (triangles) rather than by degree, which could be examined in registry follow-ups.
  • The classroom contrast suggests group size or the content of interaction (academic vs. social) may determine which formation channel dominates; a designed experiment varying group size while holding social content fixed would isolate that mechanism.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper estimates the causal effects of random assignment to university classrooms and smaller social groups on students' social networks, using fine-grained interaction data from the Copenhagen Network Study. It first presents dyadic 2SLS estimates showing that same-group assignment substantially increases various interaction measures and a composite link indicator. It then extends a Subgraph Generated Model (SUGM) with a control-function approach to separate the direct pair-level effect of sharing a group from the effect operating through triadic closure. The central empirical claim is that for social groups, the effect on network formation operates almost entirely through increased triadic closure (θ1 = 0.037, SE 0.007), with an insignificant, if anything negative, direct link effect (β1 = -0.027, SE 0.110). Classroom effects are weaker and noisier. Counterfactual simulations from the estimated SUGM and a dyadic model are compared under a welfare framework, showing that accounting for triadic closure can substantially change implied welfare.

Significance. If the central decomposition is credible, the paper makes an important contribution: it is the first to separately identify direct and triadic-closure channels of social-foci interventions using random assignment, and it demonstrates that standard dyadic models can understate the higher-order structural and welfare consequences of group-assignment policies. The empirical design is strong: genuine randomization, large first-stage F-statistics (1,311–1,539), balance tests, Borusyak–Hull recentering, dyad-robust and bootstrap inference, and extensive robustness checks across control-function orders, triad weighting, and threshold definitions. The authors also provide code and synthetic data. However, the load-bearing SUGM identification is not yet convincingly established: the nonlinear model's ability to separate the two channels is not supported by an identification proof or Monte Carlo evidence, and key unconstrained estimates violate the probability space. These issues undermine the headline claim until resolved.

major comments (4)
  1. [§4.2, Eq. (27) and Table 4] The central claim that social groups work 'almost entirely' through triadic closure requires that the NLLS objective in Eq. (27) can separately recover the direct-link coefficient β1 and the triangle-formation coefficient θ1. The paper provides no identification proof and no Monte Carlo evidence for this nonlinear model. This is not a formality: a purely dyadic DGP in which same-group pairs have higher link probabilities will also produce triangles as products of independent edge probabilities, and the incidental-link expression in Eq. (25) compounds θ1 over many triads. A simulation showing that a direct-only DGP is not misattributed to θ1>0 with β1≈0 is essential. Without it, the 'almost entirely triadic closure' conclusion is not established.
  2. [Table 4 and Table H5] The unconstrained social-group SUGM estimate of the triangle intercept is θ0 = -0.004 (SE 0.003, significant at 10%), a negative baseline triangle probability. The constrained re-estimation reported in Table H5 changes the direct-link coefficient from β1 = -0.027 to β1 = +0.057. This sign flip contradicts the text's characterization of the direct effect as 'if anything negative' (Section 4.3) and shows that the decomposition is sensitive to the model's probability-space constraints. The robustness of the headline decomposition is therefore questionable.
  3. [§4.3 and Abstract] The claim that group assignment affects network formation 'almost entirely' through triadic closure is too strong given the precision of β1. In Table 4, the social-group direct-link coefficient is -0.027 with a standard error of 0.110, giving an approximate 95% confidence interval of [-0.24, 0.19]. This interval includes substantial positive direct effects (e.g., +0.15, which would be half the total 0.32 effect). The text acknowledges the direct channel is imprecise, but the abstract and conclusions present the decomposition as definitive. The language should be tempered to reflect that the data cannot rule out a meaningful direct-link channel.
  4. [§4.1.2, Eq. (11)] The SUGM separation relies on the conditional independence assumption in Eq. (11) together with the control-function exclusions (9)–(10). If unobserved common shocks—such as a study program's social atmosphere—jointly affect triangle formation and the sorting of triads into the same group net of the first-stage errors, then θ1 will be biased and the decomposition misattributes the group effect to triadic closure. The authors note this limitation in Section 6, but given that the central conclusion depends on this assumption, a sensitivity analysis (e.g., allowing correlated unobservables within programs or using a placebo outcome) would materially strengthen the identification argument.
minor comments (5)
  1. [Abstract] Grammar: 'whether group assignment interact with triadic closure' should be 'interacts.'
  2. [Author affiliations] Author affiliation 'University of Copenhage' is missing the final 'n'.
  3. [References] There are several typos in the reference list, e.g., 'CESifo Working Ppars' and inconsistent capitalization. Please copyedit.
  4. [§5.1, Eq. (13)] The utility model uses β for the closed-triad spillover parameter, which conflicts notationally with the SUGM coefficient β1 for direct link formation. Consider renaming one of them to avoid confusion.
  5. [Figure 3] The entries in the heatmap consist of repeated stars and values; the figure would be easier to read if a color scale with a legend were used instead of printing p-values inside cells.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the SUGM decomposition is an estimated, assumption-dependent data outcome rather than an input or self-citation artifact.

full rationale

The central SUGM decomposition is not circular. Table 4's β1 and θ1 are outputs of the NLS objective in eq. (27), which fits observed link and triangle indicators through g^L (eq. 25) and g^T (eq. 26); neither coefficient is defined as, or fitted to, the other or to the dyadic total effect. The claim that social-group effects operate 'almost entirely' through triadic closure is a data outcome from Table 4 and its robustness versions (Tables H2-H5), not an imposed restriction. The identifying assumptions (eqs. 9-11) are substantive and are explicitly flagged as limitations in Section 6; they may be wrong (e.g., if unobserved common shocks remain), and the absence of a Monte Carlo or formal identification proof is a real robustness/correctness concern, but that is not circularity. The SUGM framework comes from Chandrasekhar and Jackson (2025), an external source; the paper's own prior work is cited for the CNS data, instrument-recentering precedent, or dyadic 2SLS tradition, none of which forces the sign or significance of θ1. The welfare simulations are explicitly counterfactual: parameters are estimated first, then networks are drawn from each model (Section 5.2), so the finding that the SUGM produces more triangles is a disclosed implication of its triangle-formation channel, not evidence used to identify that channel. One non-circular inconsistency is worth noting: the constrained estimates (Table H5) flip the social-group β1 from -0.027 to +0.057, contradicting the text's 'same sign' claim; this points to model instability, not to a definitional equivalence. Overall, no step in the derivation reduces by construction to its inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical or conceptual entities are postulated; 'direct' and 'triadic-closure' channels are decomposition primitives of an existing model family, not invented mechanisms with independent falsifiable handles. The main free choices are the link-measure threshold, the control-function order, the triad weight, and the welfare scenario parameters, all robustness-checked except for the SUGM cutoff sensitivity.

free parameters (4)
  • Composite-link threshold = 90th percentile within study program
    Defines the binary outcome used in both 2SLS and SUGM. Robustness across 70-97.5% thresholds is reported for 2SLS (Table B3) but not for the SUGM.
  • Control-function specification (order and coefficients) = quadratic baseline; linear and cubic robustness
    The polynomial control functions hL and hT (Appendix 6.3-6.4) are fit to the data; theta_1/beta_1 are robust across orders, so this is secondary but still fitted.
  • Triad weight omega_T in NLLS objective = 1 (baseline); 0.23 (robustness)
    Choice of weighting between dyad and triad moments in the nonlinear least squares objective; robustness check uses the dyad/triad ratio and yields similar estimates.
  • Welfare spillover parameters (gamma, lambda; U_L) = grid of scenarios; U_L normalized
    Scenario inputs in the counterfactual welfare simulation, not estimated from data; the paper reports the sensitivity surface in Figure 3.
assumptions (5)
  • domain assumption Exclusion restriction: random assignment to groups affects student interactions only through realized group membership
    Stated in Section 3 ('To be a valid instrument...'). Recentering per Borusyak-Hull addresses mechanical correlation with study-program size. Untestable; a violation would bias both 2SLS and SUGM estimates.
  • domain assumption Pair-level no-interference (SUTVA) for the dyadic 2SLS specification
    The dyadic model (eq. 1) treats each pair's outcome as a function only of that pair's treatment; triadic interference is exactly what the SUGM then introduces, so the 2SLS results should be read as reduced-form total-effect estimates.
  • domain assumption Conditional independence of link/triangle unobservables (SUGM eq. 11) and control-function exclusion (eqs. 9-10)
    Load-bearing for separating theta_1 from beta_1; acknowledged in Section 6 as an 'independence assumption inherent to the SUGM framework'. If unobserved program-level shocks remain, the decomposition is biased.
  • domain assumption Linear probability functional form for link and triangle formation (eqs. 3-4) with mean-zero unobservables
    Natural extension of the linear 2SLS model; probabilities are not bounded to [0,1], which is why constrained re-estimation (Table H5) is needed for simulations.
  • domain assumption Sample representativeness: CNS participants are exchangeable with non-participants within the randomized cohort
    Voluntary participation shrinks the social-group sample from 606 to 171 students; authors flag self-selection risk in Section 6. A violation threatens external validity more than internal validity given randomization within participants.

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Cite this review

Pith. "Pith review of Engineering Social Networks: How Initial Group Assignment Shapes Student Social Interactions." pith.science (2026). https://pith.science/paper/4PZJMXYT

@misc{pith2026260717926,
  author       = {Pith},
  title        = {Pith review of: Engineering Social Networks: How Initial Group Assignment Shapes Student Social Interactions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4PZJMXYT}},
  note         = {Machine review of arXiv:2607.17926}
}
read the original abstract

A large literature uses exogenous variation to estimate how assignment to classrooms or other groups shapes social networks. Yet most of these analyses remain dyadic, treating each link in isolation, even though ties often form through triadic closure, as a friend of a friend also becomes a friend. Using fine-grained data on phone calls, text messages, physical co-location, and social-media ties, we estimate the network formation effects of randomly assigning first-year university students to classrooms and to smaller social groups. To analyze explicitly whether group assignment interact with triadic closure, we use our random assignment to estimate a subgraph generated model of network formation. Accounting for triadic closure turns out to be crucial. For social groups in particular, group assignment affects network formation almost entirely by inducing additional triadic closure. Estimates ignoring triadic closure can thus yield misleading predictions about the network effects and benefits of group assignment policies.

Figures

Figures reproduced from arXiv: 2607.17926 by the authors.

Figure 1
Figure 1. Average weekly interactions for pairs not sharing a social group vs. pairs sharing a social group (left panel) as [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Examples of simulated networks. Color indicates studyline membership, shade indicates social group [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Mean utility difference across N = 500 simulated networks (SUGM minus dyadic), shown over the spillover strength γ and the mixing parameter λ that governs the relative weight on open- versus closed-triad spillovers. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

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Reference graph

Works this paper leans on

118 extracted references · 23 canonical work pages

  1. [1]

    Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on , pages=

    Real-time segmentation of on-line handwritten arabic script , author=. Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on , pages=. 2014 , organization=

    2014

  2. [2]

    Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of , pages=

    Fast classification of handwritten on-line Arabic characters , author=. Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of , pages=. 2014 , organization=

    2014

  3. [3]

    arXiv preprint arXiv:1804.09028 , year=

    Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications , author=. arXiv preprint arXiv:1804.09028 , year=

    arXiv

  4. [5]

    Denmark Statistics (2025 a ): BANKAKT , Accessed: 2025-03-09

    2025

  5. [6]

    --- -.1pt --- -.1pt --- (2025 b ): FAMBRUTTOINDK, Accessed: 2025-03-09

    2025

  6. [7]

    --- -.1pt --- -.1pt --- (2025 c ): OBLAKT, Accessed: 2025-03-09

    2025

  7. [8]

    Samii, Cyrus (2015): Cluster- Robust Variance Estimation for Dyadic Data , Artwork Size: 3670, 4784, 1699, 17157, 1005, 2546, 3749751, 5192296, 806, 4772, 2540, 1841, 1573, 1396 Pages: 3670, 4784, 1699, 17157, 1005, 2546, 3749751, 5192296, 806, 4772, 2540, 1841, 1573, 1396

    2015

  8. [9]

    Statistics, Denmark (2025): KURSAKT, Accessed: 2025-03-09

    2025

Show all 118 references
  1. [10]

    Christie, Sarah Cheung, Alexander H

    Whillans, Ashley V., Chelsea D. Christie, Sarah Cheung, Alexander H. Jordan, and Frances S. Chen (2017): From Misperception to Social Connection : Correlates and Consequences of Overestimating Others ’ Social Connectedness , Personality and Social Psychology Bulletin, 43 (12),...

    2017

  2. [11]

    Wold, Svante, Kim Esbensen, and Paul Geladi (1987): Principal component analysis, Chemometrics and Intelligent Laboratory Systems, 2 (1), 37--52

    1987

  3. [12]

    Cluster-

    Samii, Cyrus , collaborator =. Cluster-. 2015 , note =. doi:10.7910/DVN/OMJYE5 , abstract =

    2015 doi

  4. [13]

    Personality and Social Psychology Bulletin , author =

    From. Personality and Social Psychology Bulletin , author =. 2017 , note =. doi:10.1177/0146167217727496 , abstract =

    2017 doi

  5. [14]

    Chemometrics and Intelligent Laboratory Systems , author =

    Principal component analysis , volume =. Chemometrics and Intelligent Laboratory Systems , author =. 1987 , pages =. doi:10.1016/0169-7439(87)80084-9 , abstract =

    1987 doi

  6. [15]

    The Annals of Mathematical Statistics , author =

    The. The Annals of Mathematical Statistics , author =. 1935 , note =. doi:10.1214/aoms/1177732594 , abstract =

    1935

  7. [16]

    and Ridder, Geert and Thiemann, Petra and Zamarro, Gema , month = jul, year =

    Graham, Bryan S. and Ridder, Geert and Thiemann, Petra and Zamarro, Gema , month = jul, year =. Teacher-to-

  8. [17]

    Hidden yet quantifiable:

    Bartolomeis, Piersilvio De and Abad, Javier and Donhauser, Konstantin and Yang, Fanny , month = may, year =. Hidden yet quantifiable:

  9. [18]

    Årsrapport 2013 , url =

    2013

  10. [19]

    Chapter 4 -

    Sacerdote, Bruce , editor =. Chapter 4 -. Handbook of the. 2011 , doi =

    2011

  11. [20]

    , editor =

    Epple, Dennis and Romano, Richard E. , editor =. Chapter 20 -. Handbook of. 2011 , doi =

    2011

  12. [21]

    Zölitz, Ulf and Feld, Jan , month = aug, year =. The. doi:10.2139/ssrn.3071681 , abstract =

    doi

  13. [22]

    Annual Review of Economics , author =

    Social. Annual Review of Economics , author =. 2017 , note =. doi:10.1146/annurev-economics-063016-103625 , abstract =

    2017 doi

  14. [24]

    Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook , isbn =

    Backstrom, Lars and Kleinberg, Jon , month = feb, year =. Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook , isbn =. Proceedings of the 17th. doi:10.1145/2531602.2531642 , abstract =

  15. [25]

    Proceedings of the National Academy of Sciences , author =

    Coauthorship networks and patterns of scientific collaboration , volume =. Proceedings of the National Academy of Sciences , author =. 2004 , note =. doi:10.1073/pnas.0307545100 , abstract =

    2004 doi

  16. [26]

    American Journal of Sociology , author =

    The. American Journal of Sociology , author =. 1973 , note =

    1973

  17. [27]

    annual Review of Sociology , volume=

    Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms , author=. annual Review of Sociology , volume=. 2010 , publisher=

    2010

  18. [28]

    Psychological Science , volume=

    The negative effect of smartphone use on academic performance may be overestimated: Evidence from a 2-year panel study , author=. Psychological Science , volume=. 2020 , publisher=

    2020

  19. [29]

    Science , author =

    Exposure to ideologically diverse news and opinion on. Science , author =. 2015 , note =. doi:10.1126/science.aaa1160 , abstract =

    2015 doi

  20. [30]

    American Sociological Review , author =

    Hiring as. American Sociological Review , author =. 2012 , note =. doi:10.1177/0003122412463213 , abstract =

    2012 doi

  21. [31]

    Networks,

    Easley, David and Kleinberg, Jon , year =. Networks,

  22. [32]

    , year =

    Jackson, Matthew O. , year =. Social and

  23. [33]

    Econometrica , author =

    Nonrandom. Econometrica , author =. 2023 , note =. doi:10.3982/ECTA19367 , abstract =

    2023 doi

  24. [34]

    Instrumental

    Angrist, Joshua D and Krueger, Alan B , year =. Instrumental

  25. [35]

    Review of Economic Studies , volume=

    A network formation model based on subgraphs , author=. Review of Economic Studies , volume=. 2025 , publisher=

    2025

  26. [36]

    Labour Economics , author =

    The perils of peer effects , volume =. Labour Economics , author =. 2014 , keywords =. doi:10.1016/j.labeco.2014.05.008 , abstract =

    2014 doi

  27. [37]

    and Sacerdote, Bruce and West, James E

    Carrell, Scott E. and Sacerdote, Bruce and West, James E. , month = mar, year =. From

  28. [38]

    American Economic Journal: Applied Economics , author =

    Neighbors'. American Economic Journal: Applied Economics , author =. 2022 , keywords =. doi:10.1257/app.20200360 , abstract =

    2022 doi

  29. [39]

    Nature Communications , author =

    Exercise contagion in a global social network , volume =. Nature Communications , author =. 2017 , note =. doi:10.1038/ncomms14753 , abstract =

    2017 doi

  30. [40]

    Management Science , author =

    The. Management Science , author =. 2022 , note =. doi:10.1287/mnsc.2021.3993 , abstract =

    2022

  31. [41]

    The Review of Economic Studies , author =

    Ability. The Review of Economic Studies , author =. 2017 , pages =. doi:10.1093/restud/rdw045 , abstract =

    2017 doi

  32. [42]

    Science , author =

    Empirical. Science , author =. 2006 , note =

    2006

  33. [44]

    American Journal of Sociology , author =

    The. American Journal of Sociology , author =. 1981 , note =

    1981

  34. [45]

    Annual Review of Economics , author =

    Peer. Annual Review of Economics , author =. 2020 , note =. doi:10.1146/annurev-economics-020320-033926 , abstract =

    2020 doi

  35. [46]

    American Economic Journal: Applied Economics , author =

    Peer. American Economic Journal: Applied Economics , author =. 2019 , keywords =. doi:10.1257/app.20180286 , abstract =

    2019 doi

  36. [47]

    Scientific Data , author =

    Interaction data from the. Scientific Data , author =. 2019 , note =. doi:10.1038/s41597-019-0325-x , abstract =

    2019 doi

  37. [48]

    Journal of Abnormal Child Psychology , author =

    Peer. Journal of Abnormal Child Psychology , author =. 2005 , keywords =. doi:10.1007/s10802-005-3576-2 , abstract =

    2005 doi

  38. [50]

    The Review of Economics and Statistics , author =

    Peer. The Review of Economics and Statistics , author =. 2003 , pages =. doi:10.1162/003465303762687677 , abstract =

    2003 doi

  39. [51]

    The Quarterly Journal of Economics , author =

    Peer. The Quarterly Journal of Economics , author =. 2001 , pages =. doi:10.1162/00335530151144131 , abstract =

    2001 doi

  40. [52]

    , month = jul, year =

    Kremer, Michael and Levy, Dan M. , month = jul, year =. Peer. doi:10.3386/w9876 , abstract =

    doi

  41. [53]

    Journal of Labor Economics , author =

    Does. Journal of Labor Economics , author =. 2009 , note =. doi:10.1086/600143 , abstract =

    2009 doi

  42. [54]

    European Economic Review , author =

    Peer and social networks in job search , volume =. European Economic Review , author =. 2002 , keywords =. doi:10.1016/S0014-2921(01)00221-5 , abstract =

    2002 doi

  43. [55]

    http://www.nber.org/papers/w16039

    Christakis, Nicholas A and Fowler, James H and Imbens, Guido W and Kalyanaraman, Karthik. An Empirical Model for Strategic Network Formation. 2010. doi:10.3386/w16039 , URL = "http://www.nber.org/papers/w16039", abstract =

    2010 doi

  44. [56]

    and Strogatz, Steven H

    Watts, Duncan J. and Strogatz, Steven H. , title=. Nature , year=. doi:10.1038/30918 , url=

    doi

  45. [57]

    American Economic Journal: Applied Economics , volume=

    Neighbors' effects on university enrollment , author=. American Economic Journal: Applied Economics , volume=. 2022 , publisher=

    2022

  46. [58]

    The Quarterly Journal of Economics , volume=

    How do friendships form? , author=. The Quarterly Journal of Economics , volume=. 2006 , publisher=

    2006

  47. [59]

    The Quarterly Journal of Economics , volume=

    O brother, where start thou? Sibling spillovers on college and major choice in four countries , author=. The Quarterly Journal of Economics , volume=. 2021 , publisher=

    2021

  48. [60]

    American Educational Research Journal , volume=

    Ability grouping and student friendships , author=. American Educational Research Journal , volume=. 1985 , publisher=

    1985

  49. [61]

    Review of Economics and Statistics , volume=

    Random assignment with nonrandom peers: A structural approach to counterfactual treatment assessment , author=. Review of Economics and Statistics , volume=. 2024 , publisher=

    2024

  50. [62]

    American Economic Journal: Microeconomics , volume=

    The impact of social ties on group interactions: Evidence from minimal groups and randomly assigned real groups , author=. American Economic Journal: Microeconomics , volume=. 2012 , publisher=

    2012

  51. [63]

    2021 , institution=

    College as a marriage market , author=. 2021 , institution=

    2021

  52. [64]

    Proceedings of the national academy of sciences , volume=

    Structure and tie strengths in mobile communication networks , author=. Proceedings of the national academy of sciences , volume=. 2007 , publisher=

    2007

  53. [65]

    Proceedings of the national academy of sciences , volume=

    Inferring friendship network structure by using mobile phone data , author=. Proceedings of the national academy of sciences , volume=. 2009 , publisher=

    2009

  54. [66]

    Social Forces , volume=

    Partnership ties shape friendship networks: A dynamic social network study , author=. Social Forces , volume=. 2015 , publisher=

    2015

  55. [67]

    The Review of Economic Studies , author =

    Identification of. The Review of Economic Studies , author =. 1993 , pages =

    1993

  56. [68]

    New England journal of medicine , volume=

    The spread of obesity in a large social network over 32 years , author=. New England journal of medicine , volume=. 2007 , publisher=

    2007

  57. [69]

    PloS one , volume=

    Measuring large-scale social networks with high resolution , author=. PloS one , volume=. 2014 , publisher=

    2014

  58. [70]

    Gender homophily in job referrals: Evidence from a field study among university students

    Hederos, Karin and Sandberg, Anna and Kvissberg, Lukas and Polano, Erik. Gender homophily in job referrals: Evidence from a field study among university students. SSRN Electron. J

  59. [71]

    Network Science , author=

    Homophily in the formation and development of learning networks among university students , volume=. Network Science , author=. 2020 , pages=. doi:10.1017/nws.2020.10 , number=

    2020 doi

  60. [72]

    Imbens and Joshua D

    Guido W. Imbens and Joshua D. Angrist , journal =. Identification and Estimation of Local Average Treatment Effects , urldate =

  61. [73]

    CESifo Working Ppars , year=

    Friendship Networks and Political Opinions: A Natural Experiment among Future French Politicians , author=. CESifo Working Ppars , year=

  62. [74]

    2025 , note =

    Denmark Statistics , urldate =. 2025 , note =

    2025

  63. [75]

    American Economic Review , volume=

    Social capital and social quilts: Network patterns of favor exchange , author=. American Economic Review , volume=. 2012 , publisher=

    2012

  64. [76]

    Journal of economic literature , volume=

    The economic consequences of social-network structure , author=. Journal of economic literature , volume=. 2017 , publisher=

    2017

  65. [77]

    Econometrica , volume=

    Social networks, reputation, and commitment: evidence from a savings monitors experiment , author=. Econometrica , volume=. 2019 , publisher=

    2019

  66. [78]

    arXiv preprint arXiv:2306.15000 , year=

    Identifying Socially Disruptive Policies , author=. arXiv preprint arXiv:2306.15000 , year=

    arXiv

  67. [79]

    Journal of Economic Theory , volume=

    Homophily and long-run integration in social networks , author=. Journal of Economic Theory , volume=. 2012 , publisher=

    2012

  68. [80]

    Journal of Economic Theory , volume=

    Assortative matching with network spillovers , author=. Journal of Economic Theory , volume=. 2020 , publisher=

    2020

  69. [81]

    The Quarterly Journal of Economics , volume=

    How homophily affects the speed of learning and best-response dynamics , author=. The Quarterly Journal of Economics , volume=. 2012 , publisher=

    2012

  70. [82]

    Scientific Reports , volume=

    Short-term and long-term effects of a social network intervention on friendships among university students , author=. Scientific Reports , volume=. 2020 , publisher=

    2020

  71. [83]

    Proceedings of the National Academy of Sciences , volume=

    Integration in emerging social networks explains academic failure and success , author=. Proceedings of the National Academy of Sciences , volume=. 2019 , publisher=

    2019

  72. [84]

    Effects of missing data in social networks , journal =

    Gueorgi Kossinets , keywords =. Effects of missing data in social networks , journal =. 2006 , issn =. doi:https://doi.org/10.1016/j.socnet.2005.07.002 , url =

    2006 doi

  73. [85]

    Algan, Yann, Nicolo Dalvit, Quoc-Anh Do, Alexis Le Chapelain, and Yves Zenou (2023): Friendship Networks and Political Opinions: A Natural Experiment among Future French Politicians, CESifo Working Ppars

    2023

  74. [86]

    Altmejd, Adam, Andr \'e s Barrios-Fern \'a ndez, Marin Drlje, Joshua Goodman, Michael Hurwitz, Dejan Kovac, Christine Mulhern, Christopher Neilson, and Jonathan Smith (2021): O brother, where start thou? Sibling spillovers on college and major choice in four countries, The Qua...

    2021

  75. [87]

    Barrios-Fern \'a ndez, Andr \'e s (2022): Neighbors' effects on university enrollment, American Economic Journal: Applied Economics, 14 (3), 30--60

    2022

  76. [88]

    Bjerre-Nielsen, Andreas (2020): Assortative matching with network spillovers, Journal of Economic Theory, 187, 105028

    2020

  77. [89]

    Bjerre-Nielsen, Andreas, Asger Andersen, Kelton Minor, and David Dreyer Lassen (2020): The negative effect of smartphone use on academic performance may be overestimated: Evidence from a 2-year panel study, Psychological Science, 31 (11), 1351--1362

    2020

  78. [90]

    Booij, Adam S, Edwin Leuven, and Hessel Oosterbeek (2017): Ability Peer Effects in University : Evidence from a Randomized Experiment , The Review of Economic Studies, 84 (2), 547--578

    2017

  79. [91]

    Bramoull \'e , Yann, Sergio Currarini, Matthew O Jackson, Paolo Pin, and Brian W Rogers (2012): Homophily and long-run integration in social networks, Journal of Economic Theory, 147 (5), 1754--1786

    2012

  80. [92]

    Breza, Emily and Arun G Chandrasekhar (2019): Social networks, reputation, and commitment: evidence from a savings monitors experiment, Econometrica, 87 (1), 175--216

    2019

  81. [93]

    Bursztyn, Leonardo and Robert Jensen (2017): Social Image and Economic Behavior in the Field : Identifying , Understanding , and Shaping Social Pressure , Annual Review of Economics, 9 (Volume 9, 2017), 131--153, publisher: Annual Reviews

    2017

  82. [94]

    Chandrasekhar, Arun G and Matthew O Jackson (2025): A network formation model based on subgraphs, Review of Economic Studies, 92 (6), 3741--3787

    2025

  83. [95]

    Levy, and Jacque Eccles (2005): Peer Effects in Drug Use and Sex Among College Students , Journal of Abnormal Child Psychology, 33 (3), 375--385

    Duncan, Greg J., Johanne Boisjoly, Michael Kremer, Dan M. Levy, and Jacque Eccles (2005): Peer Effects in Drug Use and Sex Among College Students , Journal of Abnormal Child Psychology, 33 (3), 375--385

    2005

  84. [96]

    Eagle, Nathan, Alex Pentland, and David Lazer (2009): Inferring friendship network structure by using mobile phone data, Proceedings of the national academy of sciences, 106 (36), 15274--15278

    2009

  85. [97]

    Easley, David and Jon Kleinberg (2010): Networks, Crowds , and Markets : Reasoning about a Highly Connected World , Cambridge: Cambridge University Press

    2010

  86. [98]

    Romano (2011): Chapter 20 - Peer Effects in Education : A Survey of the Theory and Evidence , in Handbook of Social Economics , ed

    Epple, Dennis and Richard E. Romano (2011): Chapter 20 - Peer Effects in Education : A Survey of the Theory and Evidence , in Handbook of Social Economics , ed. by Jess Benhabib, Alberto Bisin, and Matthew O. Jackson, North-Holland, vol. 1, 1053--1163

    2011

  87. [99]

    (1981): The Focused Organization of Social Ties , American Journal of Sociology, 86 (5), 1015--1035, publisher: University of Chicago Press

    Feld, Scott L. (1981): The Focused Organization of Social Ties , American Journal of Sociology, 86 (5), 1015--1035, publisher: University of Chicago Press

    1981

  88. [100]

    Goette, Lorenz, David Huffman, and Stephan Meier (2012): The impact of social ties on group interactions: Evidence from minimal groups and randomly assigned real groups, American Economic Journal: Microeconomics, 4 (1), 101--115

    2012

  89. [101]

    Golub, Benjamin and Matthew O Jackson (2012): How homophily affects the speed of learning and best-response dynamics, The Quarterly Journal of Economics, 127 (3), 1287--1338

    2012

  90. [102]

    Graham, Bryan S., Geert Ridder, Petra Thiemann, and Gema Zamarro (2020): Teacher-to- Classroom Assignment and Student Achievement ,

    2020

  91. [103]

    Griffith, Alan (2024): Random assignment with nonrandom peers: A structural approach to counterfactual treatment assessment, Review of Economics and Statistics, 106 (3), 859--871

    2024

  92. [104]

    Hallinan, Maureen T and Aage B S rensen (1985): Ability grouping and student friendships, American Educational Research Journal, 22 (4), 485--499

    1985

  93. [105]

    Harmon, Nikolaj, Raymond Fisman, and Emir Kamenica (2019): Peer Effects in Legislative Voting , American Economic Journal: Applied Economics, 11 (4), 156--180

    2019

  94. [106]

    and Joshua D

    Imbens, Guido W. and Joshua D. Angrist (1994): Identification and Estimation of Local Average Treatment Effects, Econometrica, 62 (2), 467--475

    1994

  95. [107]

    (2008): Social and Economic Networks , Princeton University Press

    Jackson, Matthew O. (2008): Social and Economic Networks , Princeton University Press

    2008

  96. [108]

    Jackson, Matthew O, Tomas Rodriguez-Barraquer, and Xu Tan (2012): Social capital and social quilts: Network patterns of favor exchange, American Economic Review, 102 (5), 1857--1897

    2012

  97. [109]

    Kossinets, Gueorgi (2006): Effects of missing data in social networks, Social Networks, 28 (3), 247--268

    2006

  98. [110]

    Watts (2006): Empirical Analysis of an Evolving Social Network , Science, 311 (5757), 88--90, publisher: American Association for the Advancement of Science

    Kossinets, Gueorgi and Duncan J. Watts (2006): Empirical Analysis of an Evolving Social Network , Science, 311 (5757), 88--90, publisher: American Association for the Advancement of Science

    2006

  99. [111]

    Levy (2003): Peer Effects and Alcohol Use Among College Students ,

    Kremer, Michael and Dan M. Levy (2003): Peer Effects and Alcohol Use Among College Students ,

    2003

  100. [112]

    Marmaros, David and Bruce Sacerdote (2002): Peer and social networks in job search, European Economic Review, 46 (4), 870--879

    2002

  101. [113]

    --- -.1pt --- -.1pt --- (2006): How do friendships form? The Quarterly Journal of Economics, 121 (1), 79--119

    2006

  102. [114]

    McPherson, Miller, Lynn Smith-Lovin, and James M Cook (2001): Birds of a Feather : Homophily in Social Networks , Annual Review of Sociology, 27 (1), 415--444, \_eprint: https://doi.org/10.1146/annurev.soc.27.1.415

    2001 doi

  103. [115]

    Mehta, Nirav, Ralph Stinebrickner, and Todd Stinebrickner (2019): Time- Use and Academic Peer Effects in College , Economic Inquiry, 57 (1), 162--171, \_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecin.12730

    2019 doi

  104. [116]

    a ki, Jorkki Hyv \

    Onnela, J-P, Jari Saram \"a ki, Jorkki Hyv \"o nen, Gy \"o rgy Szab \'o , David Lazer, Kimmo Kaski, J \'a nos Kert \'e sz, and A-L Barab \'a si (2007): Structure and tie strengths in mobile communication networks, Proceedings of the national academy of sciences, 104 (18), 7332--7336

    2007

  105. [117]

    Rivera, Mark T, Sara B Soderstrom, and Brian Uzzi (2010): Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms, annual Review of Sociology, 36 (1), 91--115

    2010

  106. [118]

    Sacerdote, Bruce (2001): Peer Effects with Random Assignment : Results for Dartmouth Roommates *, The Quarterly Journal of Economics, 116 (2), 681--704

    2001

  107. [119]

    Sapiezynski, Piotr, Arkadiusz Stopczynski, David Dreyer Lassen, and Sune Lehmann (2019): Interaction data from the Copenhagen Networks Study , Scientific Data, 6 (1), 315, number: 1 Publisher: Nature Publishing Group

    2019

  108. [120]

    Stadtfeld, Christoph and Alex Pentland (2015): Partnership ties shape friendship networks: A dynamic social network study, Social Forces, 94 (1), 453--477

    2015

  109. [121]

    Stopczynski, Arkadiusz, Vedran Sekara, Piotr Sapiezynski, Andrea Cuttone, Mette My Madsen, Jakob Eg Larsen, and Sune Lehmann (2014): Measuring large-scale social networks with high resolution, PloS one, 9 (4), e95978

    2014

  110. [122]

    and Steven H

    Watts, Duncan J. and Steven H. Strogatz (1998): Collective dynamics of `small-world' networks, Nature, 393 (6684), 440--442

    1998

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