A New Model for Information Diffusion in Heterogeneous Social Networks
Bibliographic Data
| ID | 6169636 |
|---|---|
| Authors | Vincent Busken (0000-0002-4483-7238, Utrecht University, corresponding author), Vincent Buskens (Utrecht University), K Yamaguchi (0000-0002-9921-6412, University of Chicago) |
| Year | 1999 |
| Volume | 29 |
| Issue | 1 |
| Pages | 281-325 |
| Publication date | 1999-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methodology (JOURNAL) |
| Journal identifiers | ISSN: 0081-1750 • E-ISSN: 1467-9531 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1111/0081-1750.00067 |
| OpenAlex | W2008875430 |
| Language | EN |
| Citations received | 18 |
| References cited | 28 |
This paper discusses a new model for the diffusion of information through heterogeneous social networks. In earlier models, when information was given by one actor to another the transmitter did not retain the information. The new model is an improvement on earlier ones because it allows a transmitter of information to retain that information after telling it to somebody else. Consequently, the new model allows more actors to have information during the information diffusion process. The model provides predictions of diffusion times in a given network at the global, dyadic, and individual levels. This leads to straightforward generalizations of network measures, such as closeness centrality and betweenness centrality, for research problems that focus on the efficiency of information transfer in a network. We analyze in detail how information diffusion times and centrality measures depend on a series of network measures, such as degrees and bridges. One important finding is that predictions about the time actors need to spread information in the network differ considerably between the new and old models, while the predictions about the time needed to receive information hardly differ. Finally, some cautionary remarks are made about using the model in empirical research
Betweenness centrality · Centrality · Closeness · Data mining · Data science · Diffusion · Focus (optics · Information transfer · Network model · Process (computing · Social media · Social network (sociolinguistics · Statistics · Telecommunications · World Wide Web · Complex Network Analysis Techniques · Computer Science · Game Theory and Applications · Mathematics · Opinion Dynamics and Social Influence · Theoretical Computer Science
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Understanding Internet Usage
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A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
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Centrality in social networks conceptual clarification
The social structure of trust
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Reinterpreting network measures for models of disease transmission
Specification and Estimation of Heterogeneous Diffusion Models
Some Accelerated Failure-Time Regression Models Derived from Diffusion Process Models
Diffusion and survival models for the process of entry into marriage
The degree variance
Positions in Networks
Epidemiology and Social Networks
Adding Social Structure to Diffusion Models
Contagious Collectivities
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| Unique citing works | 18 |
|---|---|
| Citations per year | 0,69 |
| Citation span | 2000 - 2022 (23) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 18 |