Universal behavior in a generalized model of contagion.

By Peter Sheridan Dodds, Duncan J. Watts. Physical Review Letters, 2004

514 citations recorded by the source corpus.

Models of contagion arise broadly in both the biological and the social sciences, with applications ranging from the transmission of infectious diseases to the spread of cultural fads. In this Letter, we introduce a general model of contagion which, by explicitly incorporating memory of past exposures to, for example, an infectious agent, rumor, or new product, includes the main features of existing contagion models and interpolates between them. We obtain exact solutions for a simple version of the model, finding that under general conditions only three classes of collective dynamics exist. Furthermore, we find that, for a given length of memory, the class into which a particular system falls is determined by only two parameters. Our model suggests novel measures for assessing the susceptibility of a population to large contagion events, and also a possible strategy for inhibiting or facilitating them.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d08210b536781bbbbdf95e5a584c4c8623d86892

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Citations in the corpus

Virality Prediction and Community Structure in Social Networks (2013, background): “Network structure can greatly aﬀect the spreading process [15,26,27]; for example, infections with small spreading rate persist in scale-free networks [8].”
Group formation in large social networks: membership, growth, and evolution (2006, background): “There are a number of theoretical models for the diffusion of a new behavior in a social network, based on simple mechanisms in which the behavior spreads contagiously across edges; see for example [12, 21, 34] for references.”
Searching for superspreaders of information in real-world social media (2014, background): “Indeed, several approaches to locating influential spreaders are developed in the context of social science, either from the algorithm aspect [25, 26], or from the view of topology and dynamical modeling [12, 27, 28].”
Inﬂuentials, Networks, and Public Opinion Formation (background): “It has recently been shown, in fact, that SIR and threshold models are extreme cases on a continuum of possible contagion models (Dodds and Watts 2004, 2005); thus, by testing the SIR model, we effectively span a wide range of possible assumptions about binary decision models with positive…”
Influentials, Networks, and Public Opinion Formation (2007, background): “It has recently been shown, in fact, that SIR and threshold models are extreme cases on a continuum of possible contagion models (Dodds and Watts 2004, 2005); thus, by testing the SIR model, we effectively span a wide range of possible assumptions about binary decision models with positive…”
Structural diversity in social contagion (2012, background): “Mathematical models in areas including interacting particle systems (28, 29) and threshold contagion (3, 30) have explored some of the global phenomena that arise from contagion processes in networks for which the behavior at a given node has a nontrivial dependence on the full set of behaviors at n”
A framework for quantitative analysis of cascades on networks (2010, methodology): “A mathematical tool for analysis of cascades can nd extensive use anywhere where cascades are studied: anomaly and spam detection, information classication, viral marketing, epidemiological studies, computer virus spread, political and social unrest and even power transmission failure [ 8 , 9].”
Web-based experiments for the study of collective social dynamics in cultural markets (2009, unknown): “…and yields somewhat different predictions about collective behavior (Adler, 1985; Arthur, 1994; Banerjee, 1992; Bentley, Lipo, Herzog, & Hahn, 2007; Bikhchandani et al., 1992; Chung & Cox, 1994; De Vany & Lee, 2001; De Vany & Walls, 1996; Dodds & Watts, 2004; Granovetter, 1978; Watts, 2002).”
Binary-state dynamics on complex networks: pair approximation and beyond (2012, unknown): “The Bass model [58, 59, 60] for diffusion of technological innovations is an extension of the SI model, and may be similarly specialized to a network context.”
Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube (2012, background): “Social contagion broadly describes a class of phenomenon where preferences and actions of individuals are influenced by interpersonal contact, impacting the aggregate diffusion and spread of behaviors, new products, ideas, or epidemics (Dodds and Watts 2004, 2005).”