A Representative Sampling Method for Peer Encouragement Designs in Network Experiments

Published Online:https://doi.org/10.1287/mksc.2022.0409

References

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev. Modern Phys. 74(1):47–97.CrossrefGoogle Scholar
  • Aral S (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
  • Aral S, Dhillon PS (2018) Social influence maximization under empirical influence models. Nature Human Behav. 2(6):375–382.CrossrefGoogle Scholar
  • Aral S, Walker D (2011) Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Sci. 57(9):1623–1639.LinkGoogle Scholar
  • Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science (1979) 337(6092):337–341.CrossrefGoogle Scholar
  • Aral S, Walker D (2014) Tie strength, embeddedness, and social influence: A large-scale networked experiment. Management Sci. 60(6):1352–1370.LinkGoogle Scholar
  • Aronow PM, Samii C (2017) Estimating average causal effects under general interference, with application to a social network experiment. Ann. Appl. Statist. 11(4):1912–1947.CrossrefGoogle Scholar
  • Ascarza E, Ebbes P, Netzer O, Danielson M (2017) Beyond the target customer: Social effects of customer relationship management campaigns. J. Marketing Res. 54(3):347–363.CrossrefGoogle Scholar
  • Bapna R, Umyarov A (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.LinkGoogle Scholar
  • Chen Y, Yang S (2007) Estimating disaggregate models using aggregate data through augmentation of individual choice. J. Marketing Res. 44(4):613–621.CrossrefGoogle Scholar
  • Chen Y, Qi Y, Liu Q, Chien P (2018) Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks. Quant. Marketing Econom. 16(4):409–440.CrossrefGoogle Scholar
  • Chib S, Greenberg E (1995) Understanding the Metropolis-Hastings algorithm. Amer. Statist. 49(4):327–335.CrossrefGoogle Scholar
  • Eckles D, Kizilcec RF, Bakshy E (2016) Estimating peer effects in networks with peer encouragement designs. Proc. Natl. Acad. Sci. USA 113(27):7316–7322.CrossrefGoogle Scholar
  • Eckles D, Karrer B, Ugander J (2017) Design and analysis of experiments in networks: Reducing bias from interference. J. Causal Inference. 5(1):20150021.CrossrefGoogle Scholar
  • Feit EM, Berman R (2019) Test & roll: Profit-maximizing A/B tests. Marketing Sci. 38(6):1038–1058.LinkGoogle Scholar
  • Godinho de Matos M, Pedro F, Rodrigo B (2018) Target the ego or target the group: Evidence from a randomized experiment in proactive churn management. Marketing Sci. 37(5):793–811.LinkGoogle Scholar
  • Goldenberg J, Han S, Lehmann DR, Hong JW (2009) The role of hubs in the adoption process. J. Marketing 73(2):1–13.CrossrefGoogle Scholar
  • Hájek J (1971) Comment on “An essay on the logical foundations of survey sampling, part one.” Foundations Statist. Inference 236.Google Scholar
  • Hinz O, Skiera B, Barrot C, Becker JU (2011) Seeding strategies for viral marketing: An empirical comparison. J. Marketing 75(6):55–71.CrossrefGoogle Scholar
  • Holme P, Kim BJ (2002) Growing scale-free networks with tunable clustering. Phys. Rev. E 65(2):026107.CrossrefGoogle Scholar
  • Horvitz DG, Thompson DJ (1952) A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47(260):663–685.CrossrefGoogle Scholar
  • Hu Y, Van den Bulte C (2014) Nonmonotonic status effects in new product adoption. Marketing Sci. 33(4):509–533.LinkGoogle Scholar
  • Hübler C, Kriegel HP, Borgwardt K, Ghahramani Z (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
  • Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Marketing Sci. 30(2):195–212.LinkGoogle Scholar
  • Kruskal W, Mosteller F (1979) Representative sampling, III: The current statistical literature. Internat. Statist. Rev. 47(3):245–265.CrossrefGoogle Scholar
  • Kruskal W, Mosteller F (1980) Representative sampling, IV: The history of the concept in statistics, 1895-1939. Internat. Statist. Rev. 48(2):169–195.CrossrefGoogle Scholar
  • Leskovec J, Krevl A (2014) SNAP datasets: Stanford large network dataset collection. Accessed July 18, 2026, https://snap.stanford.edu/data.Google Scholar
  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (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
  • Musalem A, Bradlow ET, Raju JS (2008) Who’s got the coupon? Estimating consumer preferences and coupon usage from aggregate information. J. Marketing Res. 45(6):715–730.CrossrefGoogle Scholar
  • Rozemberczki B, Sarkar R (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.CrossrefGoogle Scholar
  • Rozemberczki B, Sarkar R (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
  • Rubin DB (1980) Randomization analysis of experimental data: The Fisher randomization test comment. J. Amer. Statist. Assoc. 75(371):591–593.Google Scholar
  • Sun T, Viswanathan S, Zheleva E (2021) Creating social contagion through firm-mediated message design: Evidence from a randomized field experiment. Management Sci. 67(2):808–827.LinkGoogle Scholar
  • Takac L, Zabovsky M (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
  • Trusov M, Bodapati AV, Bucklin RE (2010) Determining influential users in internet social networks. J. Marketing Res. 47(4):643–658.CrossrefGoogle Scholar
  • Ugander J, Karrer B, Backstrom L, Kleinberg J (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
  • Walker D, Muchnik L (2014) Design of randomized experiments in networks. Proc. IEEE 102(12):1940–1951.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.