Book chapter
Information Diffusion in Social Networks: Friendship Paradox Based Models and Statistical Inference
Modeling, Stochastic Control, Optimization, and Applications, pp.369-406
The IMA Volumes in Mathematics and its Applications, Springer International Publishing
07/17/2019
DOI: 10.1007/978-3-030-25498-8_16
Abstract
Dynamic models and statistical inference for the diffusion of information in social networks is an area which has witnessed remarkable progress in the last decade due to the proliferation of social networks. Modeling and inference of diffusion of information has applications in targeted advertising and marketing, forecasting elections, predicting investor sentiment and identifying epidemic outbreaks. This chapter discusses three important aspects related to information diffusion in social networks: (i) How does observation bias due to the friendship paradox (on average your friends have more friends than you do) and monophilic contagion (influence of friends of friends) affect the information diffusion dynamics? (ii) How can social networks adapt their structural connectivity depending on the state of information diffusion? (iii) How one can estimate the state of the network induced by information diffusion? The motivation for these three topics stems from recent results in network science and social sensing.
Details
- Title: Subtitle
- Information Diffusion in Social Networks: Friendship Paradox Based Models and Statistical Inference
- Creators
- Vikram Krishnamurthy - Cornell UniversityBuddhika Nettasinghe - Cornell University
- Resource Type
- Book chapter
- Publication Details
- Modeling, Stochastic Control, Optimization, and Applications, pp.369-406
- Publisher
- Springer International Publishing; Cham
- Series
- The IMA Volumes in Mathematics and its Applications
- DOI
- 10.1007/978-3-030-25498-8_16
- eISSN
- 2198-3224
- ISSN
- 0940-6573
- Language
- English
- Date published
- 07/17/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984423765202771
Metrics
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