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Exploring Social Contagion and Influence Dynamics: Insights from Network Science

Dive into the realm of social contagion, information diffusion, and influence dynamics through a multi-disciplinary lens. Discover how physicists, computer scientists, observers, sociologists, and computational modelers approach understanding and modeling these phenomena. Explore topics such as dynamic percolation, seeding strategies for maximum influence, dynamics of information cascades in Twitter, reinforcement signals in social contagion, and the significance of modeling homophily.

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Exploring Social Contagion and Influence Dynamics: Insights from Network Science

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  1. CS8803-NSNetwork ScienceFall 2013 Instructor: Constantine Dovrolis constantine@gatech.edu http://www.cc.gatech.edu/~dovrolis/Courses/NetSci/

  2. Disclaimers The following slides include only the figures or videos that we use in class; they do not include detailed explanations, derivations or descriptionscovered in class. Many of the following figures are copied from open sources at the Web. I do not claim any intellectual property for the following material.

  3. Outline • Social contagion, information diffusion, influence, • Differences with epidemic spreading • The physicist’s approach: social contagion as a form of dynamic percolation (Watts’ paper) • The (theoretical) computer scientist’s approach: seeding strategies for maximum influence on given network (Kempe’s paper) • The observational approach: dynamics of information cascades in Twitter (Bakshy’s paper) • The sociologist’s controlled experiment approach: the importance of reinforcement signals in social contagion (Centola’s paper) • The computational modeling approach: significance of modeling homophily (Aral’s paper)

  4. Please download the slides for this research paper from: http://www.cs.washington.edu/affiliates/meetings/talks04/kempe.pdf

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