Book contents
- Frontmatter
- Contents
- Preface
- 1 Basic Game Theory
- Part I Indirect Reciprocity
- Part II Evolutionary Games
- 6 Evolutionary Game for Cooperative Peer-to-Peer Streaming
- 7 Evolutionary Game for Spectrum Sensing and Access in Cognitive Networks
- 8 Graphical Evolutionary Game for Distributed Adaptive Networks
- 9 Graphical Evolutionary Game for Information Diffusion in Social Networks
- 10 Graphical Evolutionary Game for Information Diffusion in Heterogeneous Social Networks
- Part III Sequential Decision-Making
- Index
10 - Graphical Evolutionary Game for Information Diffusion in Heterogeneous Social Networks
from Part II - Evolutionary Games
Published online by Cambridge University Press: 01 July 2021
- Frontmatter
- Contents
- Preface
- 1 Basic Game Theory
- Part I Indirect Reciprocity
- Part II Evolutionary Games
- 6 Evolutionary Game for Cooperative Peer-to-Peer Streaming
- 7 Evolutionary Game for Spectrum Sensing and Access in Cognitive Networks
- 8 Graphical Evolutionary Game for Distributed Adaptive Networks
- 9 Graphical Evolutionary Game for Information Diffusion in Social Networks
- 10 Graphical Evolutionary Game for Information Diffusion in Heterogeneous Social Networks
- Part III Sequential Decision-Making
- Index
Summary
A huge amount of information, created and forwarded by millions of people with various characteristics, propagates through online social networks every day. Understanding the mechanisms of information diffusion over social networks is critical to various applications, including online advertisements and website management. Differently from most existing works in this area, we investigate information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world data sets. The theoretical results are confirmed by simulations. We also test the theory on the Twitter hashtag data set. We observe that the evolutionary dynamics fit the data well and can predict future diffusion data.
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- Publisher: Cambridge University PressPrint publication year: 2021