A Representative Sampling Method for Peer Encouragement Designs in Network Experiments
Published Online:28 Jul 2026https://doi.org/10.1287/mksc.2022.0409
References
- (2002) Statistical mechanics of complex networks. Rev. Modern Phys. 74(1):47–97.Crossref, Google Scholar
- (2016) Networked experiments. Bramoullé Y, Galeotti A, Rogers BW, eds. The Oxford Handbook of the Economics of Networks (Oxford University Press, New York), 376–411.Google Scholar
- (2018) Social influence maximization under empirical influence models. Nature Human Behav. 2(6):375–382.Crossref, Google Scholar
- (2011) Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Sci. 57(9):1623–1639.Link, Google Scholar
- (2012) Identifying influential and susceptible members of social networks. Science (1979) 337(6092):337–341.Crossref, Google Scholar
- (2014) Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Sci. 60(6):1352–1370.Link, Google Scholar
- (2017) Estimating average causal effects under general interference, with application to a social network experiment. Ann. Appl. Statist. 11(4):1912–1947.Crossref, Google Scholar
- (2017) Beyond the target customer: Social effects of customer relationship management campaigns. J. Marketing Res. 54(3):347–363.Crossref, Google Scholar
- (2015) Do your online friends make you pay? A randomized field experiment on peer influence in online social networks. Management Sci. 61(8):1902–1920.Link, Google Scholar
- (2007) Estimating disaggregate models using aggregate data through augmentation of individual choice. J. Marketing Res. 44(4):613–621.Crossref, Google Scholar
- (2018) Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks. Quant. Marketing Econom. 16(4):409–440.Crossref, Google Scholar
- (1995) Understanding the Metropolis-Hastings algorithm. Amer. Statist. 49(4):327–335.Crossref, Google Scholar
- (2016) Estimating peer effects in networks with peer encouragement designs. Proc. Natl. Acad. Sci. USA 113(27):7316–7322.Crossref, Google Scholar
- (2017) Design and analysis of experiments in networks: Reducing bias from interference. J. Causal Inference. 5(1):20150021.Crossref, Google Scholar
- (2019) Test & roll: Profit-maximizing A/B tests. Marketing Sci. 38(6):1038–1058.Link, Google Scholar
- (2018) Target the ego or target the group: Evidence from a randomized experiment in proactive churn management. Marketing Sci. 37(5):793–811.Link, Google Scholar
- (2009) The role of hubs in the adoption process. J. Marketing 73(2):1–13.Crossref, Google Scholar
- (1971) Comment on “An essay on the logical foundations of survey sampling, part one.” Foundations Statist. Inference 236.Google Scholar
- (2011) Seeding strategies for viral marketing: An empirical comparison. J. Marketing 75(6):55–71.Crossref, Google Scholar
- (2002) Growing scale-free networks with tunable clustering. Phys. Rev. E 65(2):026107.Crossref, Google Scholar
- (1952) A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47(260):663–685.Crossref, Google Scholar
- (2014) Nonmonotonic status effects in new product adoption. Marketing Sci. 33(4):509–533.Link, Google Scholar
- (2008) Metropolis algorithms for representative subgraph sampling. Giannotti F, Gunopulos D, Turini F, Zaniolo C, Ramakrishnan N, Wu X, eds. Proc. 8th IEEE Internat. Conf. Data Mining (IEEE Computer Society, Washington, DC), 283–292.Google Scholar
- (2011) Opinion leadership and social contagion in new product diffusion. Marketing Sci. 30(2):195–212.Link, Google Scholar
- (1979) Representative sampling, III: The current statistical literature. Internat. Statist. Rev. 47(3):245–265.Crossref, Google Scholar
- (1980) Representative sampling, IV: The history of the concept in statistics, 1895-1939. Internat. Statist. Rev. 48(2):169–195.Crossref, Google Scholar
- (2014) SNAP datasets: Stanford large network dataset collection. Accessed July 18, 2026, https://snap.stanford.edu/data.Google Scholar
- (2007) Measurement and analysis of online social networks. Dovrolis C, Roughan M, eds. Proc. 7th ACM SIGCOMM Conf. Internet Measurement (Association for Computing Machinery, New York), 29–42.Google Scholar
- (2008) Who’s got the coupon? Estimating consumer preferences and coupon usage from aggregate information. J. Marketing Res. 45(6):715–730.Crossref, Google Scholar
- (2020) Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models. d’Aquin M, Dietze S, eds. Proc. 29th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 1325–1334.Crossref, Google Scholar
- (2021) Twitch gamers: A dataset for evaluating proximity preserving and structural role-based node embeddings. Preprint, submitted February 16, https://arxiv.org/abs/2101.03091v2.Google Scholar
- (1980) Randomization analysis of experimental data: The Fisher randomization test comment. J. Amer. Statist. Assoc. 75(371):591–593.Google Scholar
- (2021) Creating social contagion through firm-mediated message design: Evidence from a randomized field experiment. Management Sci. 67(2):808–827.Link, Google Scholar
- (2012) Data analysis in public social networks. Proc. Internat. Scientific Conf. Internat. Workshop Present Day Trends Innovations (The State Higher School of Computer Science and Business Administration, Łomża, Poland).Google Scholar
- (2010) Determining influential users in internet social networks. J. Marketing Res. 47(4):643–658.Crossref, Google Scholar
- (2013) Graph cluster randomization: Network exposure to multiple universes. Ghani R, Senator TE, Bradley P, Parekh R, He J, eds. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 329–337.Google Scholar
- (2014) Design of randomized experiments in networks. Proc. IEEE 102(12):1940–1951.Crossref, Google Scholar

