Analysis of Information Spread on SNSs Based on Strong Correlation Assumption
Analysis of Information Spread on SNSs Based on Strong Correlation Assumption
复制标题
基于强相关假设的SNS信息传播分析
DOI：
10.1109/icnc47757.2020.9049806
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Minamikawa Masato
中科院分区：
文献类型：
--
作者：
Shioda Shigeo;Minamikawa Masato
本文使用易感者-感染者-恢复 (SIR) 模型研究了 Twitter 等社交网络服务 (SNS) 上信息传播的动态。通常，SIR模型无法精确求解，因此之前的大多数研究都基于这样的假设：节点具有目标信息的概率必须独立于其邻居是否具有该信息来考虑。相反，我们在此提出了一种基于称为“强相关假设”的假设的不同方法，其中节点具有目标信息的概率与其相邻节点是否具有该信息强相关。然后，我们基于强相关性假设分析了 SIR 模型的信息传播，并表明使用强相关性假设可以比节点独立性假设更准确地分析信息传播。
This paper investigates the dynamics of information spread on social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. Normally, the SIR model cannot be solved precisely, so most previous studies have been based on the assumption that the probability of a node having the target information must be considered independently of whether or not its neighbors have that information. In contrast, we herein propose a different approach based on an assumption called the “strong correlation assumption”, in which the probability of a node having the target information is strongly correlated with whether its neighboring nodes have that information. We then analyze the information spread of the SIR model based on our strong correlation assumption and show that the use of the strong correlation assumption makes it possible to analyze information spreads much more accurately than the node independence assumption.