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Social influence and spread dynamics in social networks
Xiaolong Zheng1, Yongguang Zhong2, Daniel Zeng1
11State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China.
Frontiers of Computer Science
|April 15, 2020
Summary
This review surveys social influence and spread dynamics in social networks, covering empirical studies, models, and computational experiments to understand personal influence and spread patterns in large systems.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Epidemiology
Background:
- Social networks are key to information, epidemic, and behavior spread.
- Interest in social influence and spread dynamics is rapidly growing.
- A comprehensive review of this field is lacking.
Purpose of the Study:
- To provide a comprehensive review of social influence and spread dynamics.
- To synthesize current empirical studies and computational experiments.
- To offer insights into personal influence and spread patterns.
Main Methods:
- Review of significant empirical studies on real social systems.
- Description of macro- and micro-level social models.
- Emphasis on computational experiments for validation.
Main Results:
- Presentation of network construction methods and measures.
- Summary of empirical results from real social systems.
- Highlighting of current research in computational experiments.
Conclusions:
- The paper offers insights into personal influence and spread patterns.
- Understanding spread dynamics in large-scale social systems is crucial.
- Bridging real data and simulations is key for validation.
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