Inference on social graphs

Heterogeneous media and controls

Nothing but chaos awaiting filing here for now.

Fun keywords: Egocentric sampling, friendship paradox, majority illusion. 🏗

Majority Illusions and filter bubbles

In homophilic networks (0.5 ≤ h ≤ 1), the minority overestimates their own size (filter bubble) and the majority underestimates the size of the minority. The insets show the same information on log scale to make the amount of underestimation and overestimation comparable. As group sizes become more disproportionate, perception bias increases. (Lerman, Yan, and Wu 2016)

This insight is one of those ones that seems trivial in hindsight, but people are terrible at articulating in advance.

To file

Michele Coscia.

Cosma Shalizi, Return of “Homophily, Contagion, Confounding: Pick Any Three”, or, The Adventures of Irene and Joey Along the Back-Door Paths and Experiments on Social Networks. See also his Neutral cultural networks stuff.

My colleague un UNSW, Pavel Krivitsky is highly productive in this area in this area, especially with Exponential family random graph model. (Hunter, Krivitsky, and Schweinberger 2012; Kolaczyk and Krivitsky 2015; Krivitsky and Morris 2017; Krivitsky et al. 2009; Krivitsky and Handcock 2014)

For models, specifically, of actual disease contagion, see Shalizi’s review of Kiss, Miller, and Simon (2017).

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Aral, Sinan, Lev Muchnik, and Arun Sundararajan. 2009. “Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks.” Proceedings of the National Academy of Sciences 106 (51): 21544–9. https://doi.org/10.1073/pnas.0908800106.

Bakshy, Eytan, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. “The Role of Social Networks in Information Diffusion.” In Proceedings of the 21st International Conference on World Wide Web, 519–28. WWW ’12. Lyon, France: ACM. https://doi.org/10.1145/2187836.2187907.

Barbieri, Nicola, Francesco Bonchi, and Giuseppe Manco. 2013. “Cascade-Based Community Detection.” In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 33–42. WSDM ’13. Rome, Italy: ACM. https://doi.org/10.1145/2433396.2433403.

Bonchi, Francesco, Francesco Gullo, Bud Mishra, and Daniele Ramazzotti. 2018. “Probabilistic Causal Analysis of Social Influence.” In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 1003–12. CIKM ’18. Torino, Italy: ACM. https://doi.org/10.1145/3269206.3271756.

Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. 2012. “A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489 (7415): 295–98. https://doi.org/10.1038/nature11421.

Cai, Diana, Trevor Campbell, and Tamara Broderick. 2016. “Edge-Exchangeable Graphs and Sparsity.” In Proceedings of the 30th International Conference on Neural Information Processing Systems, 4249–57. NIPS’16. Barcelona, Spain: Curran Associates Inc. http://papers.nips.cc/paper/6586-edge-exchangeable-graphs-and-sparsity.pdf.

Cha, Meeyoung, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. 2010. “Measuring User Influence in Twitter: The Million Follower Fallacy.” In Fourth International AAAI Conference on Weblogs and Social Media. https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1538.

Coscia, Michele. 2017. “Popularity Spikes Hurt Future Chances for Viral Propagation of Protomemes.” Communications of the ACM 61 (1): 70–77. https://doi.org/10.1145/3158227.

Crane, Harry, and Walter Dempsey. 2016. “A Framework for Statistical Network Modeling,” December. http://arxiv.org/abs/1509.08185.

———. 2018. “Edge Exchangeable Models for Interaction Networks.” Journal of the American Statistical Association 113 (523): 1311–26. https://doi.org/10.1080/01621459.2017.1341413.

———. 2019. “Relational Exchangeability.” Journal of Applied Probability 56 (1): 192–208. https://doi.org/10.1017/jpr.2019.13.

Goel, Sharad, Ashton Anderson, Jake Hofman, and Duncan J. Watts. 2015. “The Structural Virality of Online Diffusion.” Management Science, July, 150722112809007. https://doi.org/10.1287/mnsc.2015.2158.

Gomez-Rodriguez, Manuel, Jure Leskovec, and Andreas Krause. 2012. “Inferring Networks of Diffusion and Influence.” ACM Trans. Knowl. Discov. Data 5 (4): 21:1–21:37. https://doi.org/10.1145/2086737.2086741.

Gonzalez-Bailon, Sandra. 2009. “Opening the Black Box of Link Formation: Social Factors Underlying the Structure of the Web.” Social Networks 31 (4): 271–80. https://doi.org/10.1016/j.socnet.2009.07.003.

Goyal, Amit, Francesco Bonchi, and Laks V. S. Lakshmanan. 2011. “A Data-Based Approach to Social Influence Maximization.” In Proc. VLDB Endow., 5:73–84. https://doi.org/10.14778/2047485.2047492.

Goyal, Amit, Francesco Bonchi, and Laks V.S. Lakshmanan. 2010. “Learning Influence Probabilities in Social Networks.” In Proceedings of the Third ACM International Conference on Web Search and Data Mining, 241–50. WSDM ’10. New York, New York, USA: ACM. https://doi.org/10.1145/1718487.1718518.

Greenland, Sander, and James M Robins. 2009. “Identifiability, Exchangeability and Confounding Revisited.” Epidemiologic Perspectives & Innovations : EP+I 6 (September): 4. https://doi.org/10.1186/1742-5573-6-4.

Guille, Adrien, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. 2013. “Information Diffusion in Online Social Networks: A Survey.” SIGMOD Rec. 42 (2): 17–28. https://doi.org/10.1145/2503792.2503797.

Hunter, David R., Pavel N. Krivitsky, and Michael Schweinberger. 2012. “Computational Statistical Methods for Social Network Models.” Journal of Computational and Graphical Statistics 21 (4): 856–82. https://doi.org/10.1080/10618600.2012.732921.

Jackson, Matthew O. 2014. “Networks in the Understanding of Economic Behaviors.” Journal of Economic Perspectives 28 (4): 3–22. https://doi.org/10.1257/jep.28.4.3.

———. 2018. “The Friendship Paradox and Systematic Biases in Perceptions and Social Norms.” Journal of Political Economy 127 (2): 777–818. https://doi.org/10.1086/701031.

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

———. 2009. “Social Structure, Segregation, and Economic Behavior.” Presented as the Nancy Schwartz Memorial Lecture.

Kempe, David, Jon Kleinberg, and Éva Tardos. 2003. “Maximizing the Spread of Influence Through a Social Network.” In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–46. KDD ’03. Washington, D.C.: ACM. https://doi.org/10.1145/956750.956769.

Kiss, István Z., Joel Miller, and Péter L. Simon. 2017. Mathematics of Epidemics on Networks: From Exact to Approximate Models. Interdisciplinary Applied Mathematics. New York, NY: Springer International Publishing. https://doi.org/10.1007/978-3-319-50806-1.

Kitsak, Maksim. n.d. “Identifying Influential Spreaders in Complex Networks.” Accessed June 24, 2019. https://www.academia.edu/14492654/Identifying_influential_spreaders_in_complex_networks.

Kolaczyk, Eric D., and Pavel N. Krivitsky. 2015. “On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry.” Statistical Science : A Review Journal of the Institute of Mathematical Statistics 30 (2): 184–98. https://doi.org/10.1214/14-STS502.

Krivitsky, Pavel N., and Mark S. Handcock. 2014. “A Separable Model for Dynamic Networks.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1): 29–46. https://doi.org/10.1111/rssb.12014.

Krivitsky, Pavel N., Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff. 2009. “Representing Degree Distributions, Clustering, and Homophily in Social Networks with Latent Cluster Random Effects Models.” Social Networks 31 (3): 204–13. https://doi.org/10.1016/j.socnet.2009.04.001.

Krivitsky, Pavel N., and Martina Morris. 2017. “Inference for Social Network Models from Egocentrically Sampled Data, with Application to Understanding Persistent Racial Disparities in Hiv Prevalence in the Us.” The Annals of Applied Statistics 11 (1): 427–55. https://doi.org/10.1214/16-AOAS1010.

Lee, Eun, Fariba Karimi, Hang-Hyun Jo, Markus Strohmaier, and Claudia Wagner. 2017. “Homophily Explains Perception Biases in Social Networks.” arXiv:1710.08601 [Physics], October. http://arxiv.org/abs/1710.08601.

Lerman, Kristina, Xiaoran Yan, and Xin-Zeng Wu. 2016. “The "Majority Illusion" in Social Networks.” PLOS ONE 11 (2): e0147617. https://doi.org/10.1371/journal.pone.0147617.

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Liu, Ka-Yuet, Marissa King, and Peter S. Bearman. 2010. “Social Influence and the Autism Epidemic.” American Journal of Sociology 115 (5): 1387. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2927813/.

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Noel, Hans, and Brendan Nyhan. 2011. “The ‘Unfriending’ Problem: The Consequences of Homophily in Friendship Retention for Causal Estimates of Social Influence.” Social Networks 33 (3): 211–18. https://doi.org/10.1016/j.socnet.2011.05.003.

Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman. 2019. “Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.” Frontiers in Big Data 2. https://doi.org/10.3389/fdata.2019.00013.

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