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Part III - Making Structural Predictions

Published online by Cambridge University Press:  21 September 2023

Craig M. Rawlings
Duke University, North Carolina
Jeffrey A. Smith
Nova Scotia Health Authority
James Moody
Duke University, North Carolina
Daniel A. McFarland
Stanford University, California
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Network Analysis
Integrating Social Network Theory, Method, and Application with R
, pp. 299 - 420
Publisher: Cambridge University Press
Print publication year: 2023

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Suggested Further Reading

The literature on statistical models for networks is vast and has grown dramatically in the last fifteen years. Here, we list a handful of key contemporary references and classic foundational pieces, which should help those interested to launch a deeper exploration.

Anderson, Carolyn J., Wasserman, Stanley, and Crouch, Bradley. 1999. “A p* Primer: Logit Models for Social Networks.” Social Networks 21: 3766. (An early paper on the ERGM/p* approach that still provides a good intuitive understanding of how effects are coded and modeled.)CrossRefGoogle Scholar
Butts, Carter T. 2008. “A Relational Events Framework for Social Action.” Sociological Methodology 38: 155200. (Introduces a family of models for dynamic event data and sparked much recent work on modeling dynamic networks generally.)CrossRefGoogle Scholar
Cranmer, Skyler J., Desmarais, Bruce A, and Morgan, Jason W. 2020. Inferential Network Analysis. Cambridge: Cambridge University Press. (An excellent overview of ERGM and latent-space models, with detailed examinations of model fit and degeneracy issues and multiple data-type extensions.)CrossRefGoogle Scholar
Duxbury, Scott. 2022. Longitudinal Network Models. Thousand Oaks, CA: Sage. (Provides excellent background and instruction on the most common statistical models for longitudinal network data.)Google Scholar
Frank, Ove, and Strauss, David. 1986. “Markov Graphs.” Journal of the American Statistical Association 81: 832–42. (A classic reference that provides the statistical foundations for graph dependencies necessary for parameter estimation on network properties.)CrossRefGoogle Scholar
Holland, Paul W., and Leinhardt, Samuel. 1981. “An Exponential Family of Probability Distributions for Directed Graphs.” Journal of the American Statistical Association 76: 3350. (Classic work that sets the stage for the growth of network statistical models in the 1990s.)Google Scholar
Kolaczyk, Eric D. 2009. Statistical Analysis of Network Data. New York: Springer Press. (A comprehensive and rigorous overview of network models.)CrossRefGoogle Scholar
Kuskova, Valentina, and Wasserman, Stanley. 2020. “An Introduction to Statistical Models for Networks.” Pp. 219–33 in The Oxford Handbook of Social Networks, edited by Ryan, Light and Moody, James. New York: Oxford University Press. (Provides an excellent history and general overview of statistical modeling for networks.)Google Scholar
Lusher, Dean, Koskinen, Johan, and Robins, Gary (eds.). 2012. Exponential Random Graph Models for Social Networks: Theory, Methods and Applications. New York: Cambridge University Press. (This edited volume includes clear explanations and mathematical foundations for ERGMs and extensions. Includes numerous applications that provide guidance on the practical use and interpretation of complicated models. See also Lusher et al. 2020.)CrossRefGoogle Scholar
Lusher, Dean, Wang, Peng, Brennecke, Julia et al. 2020. “Advances in Exponential Random Graph Models.” Pp. 234–53 in The Oxford Handbook of Social Networks, edited by Light, Ryan and Moody, James. New York: Oxford University Press.Google Scholar
Statnet Development Team (Krivitsky, Pavel N, Handcock, Mark S, Hunter, David R et al.). 2003–20. statnet: Software tools for the Statistical Modeling of Network Data. (This group of researchers has been pushing the development of statistical models for networks for nearly twenty years. The website includes multiple tutorials and further references.)Google Scholar
Wasserman, Stanley. 1977. “Random Directed Graph Distributions and the Triad Census in Social Networks.” Journal of Mathematical Sociology 5: 6186. (An early paper on random graph distributions, essential for testing triadic distributions against underlying volume features.)CrossRefGoogle Scholar

Suggested Further Reading

Acerbi, Alberto, Mesoudi, Alex, and Smolla, Marco. 2022. Individual-Based Models of Cultural Evolution. A Step-by-Step Guide Using R. London: Routledge. (An interesting and practical guide to using simulation to investigate cultural diffusion.)CrossRefGoogle Scholar
Armbruster, Benjamin, Wang, Li, and Morris, Martina. 2017. “Forward Reachable Sets: Analytically Derived Properties of Connected Components for Dynamic Networks.” Network Science 5: 328–54. (See Moody 2000 for note.)CrossRefGoogle ScholarPubMed
Barabási, Albert-László, and Albert, Réka. 1999. “Emergence of Scaling in Random Networks.” Science 286: 509–12. (An important early paper showing that long-tail or “scale-free” degree distributions were common and can have dramatic effects on ability to control spread of disease, although many of the more dramatic empirical implications are tempered once actual empirical limits are taken into account. See also Jones and Handcock 2003; Pastor-Satorras and Vespignani 2001.)CrossRefGoogle ScholarPubMed
Burt, Ronald S. 1987. “Social Contagion and Innovation: Cohesion versus Structural Equivalence.” American Journal of Sociology 92(6): 1287–335. (A classic work distinguishing connectionist and positional mechanisms to network diffusion.)CrossRefGoogle Scholar
Centola, Damon. 2018. How Behavior Spreads: The Science of Complex Contagions. Princeton, NJ: Princeton University Press. (An engaging and readable fleshing-out of the complex contagion ideas developed over multiple prior papers and contexts.)Google Scholar
Coleman, James S., Katz, Elihu, and Menzel, Herbert. 1957. “The Diffusion of an Innovation among Physicians.” Sociometry 20: 253–70. (An excellent example of thinking through how innovations move through closed populations. This paper has become a classic reference work, although reanalysis has cast doubt on some of the original conclusions.)CrossRefGoogle Scholar
Jones, James Holland, and Handcock, Mark S.. 2003. “An Assessment of Preferential Attachment as a Mechanism for Human Sexual Network Formation.” Proceedings of the Royal Society B 270: 1123–28.Google Scholar
Klovdahl, A., Potterat, J., Woodhouse, D. et al. 1994. “Social Networks and Infectious Disease: The Colorado Springs Study.” Social Science & Medicine 38(1): 7988. (The Colorado Springs Study was a game-changer for understanding sexual networks and disease risk. The team published numerous papers on different aspects of drug and sex networks – a must-read body of work for anyone working in STD or slow-to-spread disease diffusion.)CrossRefGoogle ScholarPubMed
Liu, Ka-Yuet, King, Marissa, and Bearman, Peter S.. 2010. “Social Influence in the Autism Epidemic.” American Journal of Sociology 115: 1387–434. (Exemplar use of administrative records to infer diffusion processes.)CrossRefGoogle ScholarPubMed
Moody, James. 2000. “The Importance of Relationship Timing for Diffusion.” Social Forces 81: 2556. (Identifies the underlying path limits to diffusion potential in dynamic networks. See also Armbruster, Wang, & Morris 2017.)CrossRefGoogle Scholar
Morris, Martina, and Kretzschmar, Mirjam. 1997. “Concurrent Partnerships and the Spread of HIV.” AIDS 11: 641–48. (A touchstone citation for the effects of concurrency, which has generated a new set of ideas on how relational timing constrains diffusion, sparked much debate in the applied HIV world over mechanisms and effect sizes.)CrossRefGoogle ScholarPubMed
Newman Mark. 2002. “Spread of Epidemic Disease on Networks.” Physical Review E 66: 016128. (Outlines some of the base in-the-limit sorts of models for diffusion conditional on graph structure.)CrossRefGoogle Scholar
Pastor-Satorras, Romualdo, and Vespignani, Alessandro. 2001. “Epidemic Spreading in Scale-Free Networks.” Physical Review Letters 86: 3200. (See the note to Barabási & Albert 1999.)CrossRefGoogle ScholarPubMed
Rogers, Everett M. 2003. Diffusion of Innovations, 5th ed. New York: Simon & Schuster. (Arguably the most influential book on ideational diffusion.)Google Scholar
Valente, Thomas. 1995. Network Models of the Diffusion of Innovations. New York: Hampton Press. (This is the network-based successor to Everett Rogers’ original Diffusion of Innovations work and is necessary reading for anyone interested in ideational diffusion.)Google Scholar

Suggested Further Reading

adams, jimi, and Schaefer, David R. 2016. “How Initial Prevalence Moderates Network-Based Smoking Change: Estimating Contextual Effects with Stochastic Actor-Based Models.” Journal of Health and Social Behavior 57: 2238. (An excellent model paper for using SOAMs over multiple waves and multiple contexts.)Google Scholar
An, Weihua, Beauvile, Roberson, and Rosche, Benjamin. 2022. “Causal Network Analysis.” Annual Review of Sociology 48: 2341. (An excellent overview of the identification problem of selection or influence in empirical work on peer influence.)CrossRefGoogle Scholar
Aral, Sinan, Muchnik, Lev, and Sundararajan, Arun. 2009. “Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks.” Proceedings of the National Academy of Sciences 106: 21544–49. (An excellent use of instrumental variables for identifying causal effect of peers.)Google Scholar
Christakis, Nicholas A., and Fowler, James H.. 2007. “The Spread of Obesity in a Large Social Network over 32 Years.” New England Journal of Medicine 357: 370–79. (Arguably the most influential paper on peer influence in the last twenty years, sparking numerous debates and wide methodological investigations into network causal identification.)CrossRefGoogle Scholar
Flache, Andreas, Mäs, Michael, Feliciani, Thomas et al. 2017. “Models of Social Influence: Towards the Next Frontiers.” Journal of Artificial Societies and Social Simulation 20(4): 2. (A useful review of mainly simulation-based attempts to model social influence processes based on various theoretical accounts.)CrossRefGoogle Scholar
Friedkin, Noah E., and Johnsen, Eugene C.. 1998. A Structural Theory of Social Influence. New York: Cambridge University Press. (The foundational theoretical work on peer influence in networks; most other subsequent works have been elaborations on this base model.)CrossRefGoogle Scholar
Friedkin, Noah E., and Johnsen, Eugene C.. 2011. Social Influence Network Theory: A Sociological Examination of Small Group Dynamics. New York: Cambridge University Press. (An extension and application of their earlier work on formal models for peer influence.)CrossRefGoogle Scholar
Friedkin, Noah E., Proskurnikov, Anton V., Tempo, Roberto, and Parsegov, Sergey E.. 2016. “Network Science on Belief System Dynamics under Logic Constraints.” Science 354: 321–26. (Extends the interpersonal peer influence model among groups to include logical constraints on the beliefs themselves.)CrossRefGoogle ScholarPubMed
Kandel, Denise B. 1978. “Homophily, Selection, and Socialization in Adolescent Friendship Pairs.” American Journal of Sociology 48: 427–36. (One of the key early papers to highlight issues related to selection and overestimation of peer effects from respondents’ self-reports; nicely outlines the logic of influence identification.)Google Scholar
Papachristos, Andrew. 2009. “Murder by Structure: Dominance Relations and the Social Structure of Gang Homicide.” American Journal of Sociology 115: 74128. (Uses unique data on arrests to map and then model the spread of violence in Chicago.)CrossRefGoogle ScholarPubMed
Sacerdote, Bruce. 2001. “Peer Effects with Random Assignment: Results for Dartmouth Roommates.” The Quarterly Journal of Economics 116: 681704. (An early example of using randomization on networks to identify peer influence; sparked numerous other similar assessments.)Google Scholar
Snijders, Tom A. B., Van de Bunt, Gerhard G., and Steglich, Christian E. G.. 2020. “Introduction to Stochastic Actor-Based Models for Network Dynamics.” Social Networks 32(1): 4460. (A clear and rigorous introduction to SAOMs by the team that developed the approach.)CrossRefGoogle Scholar

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