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14 - Models for Network Diffusion

from Part III - Making Structural Predictions

Published online by Cambridge University Press:  21 September 2023

Craig M. Rawlings
Affiliation:
Duke University, North Carolina
Jeffrey A. Smith
Affiliation:
Nova Scotia Health Authority
James Moody
Affiliation:
Duke University, North Carolina
Daniel A. McFarland
Affiliation:
Stanford University, California
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Summary

Having lived through a global pandemic, or more trivially, having seen online memes “go viral,” we are all intuitively familiar with the spread of things through network ties. Diseases, memes, used books, and cash are ready examples of things passed from one person to another. Somewhat less familiar, perhaps, is that a fundamentally similar mechanism underlies many of our social behaviors. Understanding such processes is therefore related to understanding how anything – information, rumors, diseases, and so on – diffuses through a system. Key questions include: How does a network structure as a whole (its topology) affect the diffusion process? And how does a node’s position in this structure affect the likelihood of transmitting and receiving flows?

Type
Chapter
Information
Network Analysis
Integrating Social Network Theory, Method, and Application with R
, pp. 340 - 363
Publisher: Cambridge University Press
Print publication year: 2023

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References

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.)Google Scholar
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.)Google Scholar
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.)Google 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.)Google 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.)Google Scholar
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.)Google Scholar
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.)Google 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.)Google 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.)Google Scholar
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

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