摘要
社交网络中的级联规模分布可以刻画信息在社交网络中流行的程度。许多研究表明,级联规模分布遵循重尾分布;导致这种高度偏态分布的根本原因缺乏定量实验分析。基于随机异质信息传播模型(SVFR,Susceptible View Forward Removed),进行大量计算实验,探讨信息吸引力和影响力对社交网络信息级联规模分布的影响。结果表明信息影响力和吸引力的均值较小时,级联规模服从幂律分布,且方差越大,尾部越重。该发现从仿真的角度阐明了信息吸引力和影响力对特定社交网络中信息流行程度的影响,较高吸引力和影响力的信息更容易在社交网络中广泛传播。
The distribution of the cascade size can capture the distribution of popularity of a social network. Numerous studies have shown that the cascade size distribution follows fat-tail distributions, including power-law distribution and bimodal distribution; The underlying characteristic of this highly skewed distribution lacks quantitative experimental analysis. Based on the heterogeneous stochastic information dissemination model, namely the SVRF model, this paper examines the impact of the information attractiveness and influence on the cascade size distribution through lots of computational experiments. We find that when the mean value of the information influence and attractiveness is small, the cascade sizes follow a power-law distribution, and the larger the variance, the heavier the tail. Our findings quantitatively clarify the role of information attractiveness and influence on the distribution of popularity in social networks. The attractive and influential information is more likely to spread widely in social networks.
作者
董健
陈彬
刘亮
艾川
张芳
邱晓刚
Dong Jian, Chen Bin, Liu Liang, Ai Chuan, Zhang Fang, Qiu Xiaogang(National University of Defense Teehnology, Changsha 410073, China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第10期3624-3631,共8页
Journal of System Simulation
基金
国家重点研发计划重点专项(2017YF C1200300)
国家自然科学基金(71673292
71673294)
国家社会科学基金(17CGL047)
关键词
影响力
吸引力
级联规模分布
信息传播
attractiveness
influence
cascade size distribution
information dissemination
