Inference on social graphs

Heterogeneous media and controls


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Fun keywords: Egocentric sampling, friendship paradox, majority illusion, and the analysis of projectivity. 🏗

Connection to statistical relational learning

I cannot help but notice that the discussions of changing probabilistic domain, and unusual assumptions about exchangeability/projectivity are reminiscent of inference on social graphs. Connections?

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. Related, perhaps a consequence of this, is pluralistic ignorance

Confounding on graphs

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

My colleague at UNSW, Pavel Krivitsky is highly productive in this area, especially with the exponential family random graph (pronounced “ergum”.) model, and I will list the articles he wrote here so that I can pester him for details: (Hunter, Krivitsky, and Schweinberger 2012; Kolaczyk and Krivitsky 2015; Krivitsky and Morris 2017; Krivitsky et al. 2009; Krivitsky and Handcock 2014)

To file

Michele Coscia. Michele Coscia’s new paper uses a graph Laplacian to calculate an approximate Earth mover distance over a graph topology. (buzzword use case: inferring graph transmission rate of a disease interpretably). This looks simple; surely it must be a known result in optimal transport metric studies?

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

Acemoglu, Daron, and Asuman Ozdaglar. 2011. “Opinion Dynamics and Learning in Social Networks.” Dynamic Games and Applications 1 (1): 3–49. https://doi.org/10.1007/s13235-010-0004-1.

Achlioptas, Dimitris, Aaron Clauset, David Kempe, and Cristopher Moore. 2005. “On the Bias of Traceroute Sampling: Or, Power-Law Degree Distributions in Regular Graphs.” In Proceedings of the Thirty-Seventh Annual ACM Symposium on Theory of Computing, 694–703. STOC ’05. New York, NY, USA: ACM. https://doi.org/10.1145/1060590.1060693.

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

———. 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.

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. 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, NY, USA: ACM. https://doi.org/10.1145/1718487.1718518.

———. 2011. “A Data-Based Approach to Social Influence Maximization.” In Proc. VLDB Endow., 5:73–84. https://doi.org/10.14778/2047485.2047492.

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.

Harris, Jenine K. 2013. An Introduction to Exponential Random Graph Modeling. SAGE Publications. http://books.google.com?id=FVd2AwAAQBAJ.

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.

Iribarren, Jose Luis, and Esteban Moro. 2009. “Information Diffusion Epidemics in Social Networks.” Physical Review Letters 103 (3): 038702. https://doi.org/10.1103/PhysRevLett.103.038702.

Iribarren, José Luis, and Esteban Moro. 2009. “Impact of Human Activity Patterns on the Dynamics of Information Diffusion.” Physical Review Letters 103 (3): 038702. https://doi.org/10.1103/PhysRevLett.103.038702.

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, February. https://doi.org/10.2139/ssrn.1530885.

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. New York, NY, USA: 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.

Leskovec, Jure. 2012. “Information Diffusion and External Influence in Networks,” June. http://arxiv.org/abs/1206.1331.

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/.

Lyons, Russell. 2011. “The Spread of Evidence-Poor Medicine via Flawed Social-Network Analysis.” Statistics, Politics, and Policy 2 (1). https://doi.org/10.2202/2151-7509.1024.

Madar, N., T. Kalisky, R. Cohen, D. ben-Avraham, and S. Havlin. 2004. “Immunization and Epidemic Dynamics in Complex Networks.” The European Physical Journal B 38 (2): 269–76. https://doi.org/10.1140/epjb/e2004-00119-8.

Malinsky, Daniel, Ilya Shpitser, and Thomas Richardson. 2019. “A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects,” March. http://arxiv.org/abs/1903.03662.

Morozova, Olga, Ted Cohen, and Forrest W. Crawford. 2018. “Risk Ratios for Contagious Outcomes.” Journal of the Royal Society Interface 15 (138): 20170696. https://doi.org/10.1098/rsif.2017.0696.

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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———. 2010. “Conditional Marginalization for Exponential Random Graph Models.” The Journal of Mathematical Sociology 34 (4): 239–52. https://doi.org/10.1080/0022250X.2010.485707.

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