基于人类动力学的社交网络信息传播实证分析与建模研究
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摘要
随着互联网技术和移动通信技术的日益成熟,基于互动服务的新型社交网络应用得到了前所未有的发展机会。社交网络已经成为人们传播信息的工具,以及分享、沟通和交流的重要平台。不同于传统的信息传播方式,社交网络用户的行为模式和社交网络的结构特征使得信息的传播过程变得更复杂和不确定。并且,在传统信息传播建模中过于简化的模型假设和简单的理论方法难以刻画真实社交网络中信息传播的演进过程。鉴于此,本文结合人类动力学、复杂网络理论和传播动力学等学科的思想和方法,对社交网络环境下的信息传播进行实证分析和建模研究。本文的实证分析结果和理论预测结果基本一致,具有较高的实际价值和科研价值。
论文主要的研究内容和结论包括以下四个方面：
(1)研究了新浪微博用户的爆发性评论方式对微博信息流行度的影响。本文对新浪微博的真实网络数据统计研究发现,新浪微博在线用户评论信息的行为具有异质性,表现在两次连续的评论时间间隔服从幂律分布。因此本文基于新浪微博用户评论行为异质性的假设,对微博信息流行度消亡行为进行建模,并给出理论分析。得到的主要结论：当用户评论时间间隔幂指数越大,则评论爆发性越强,大部分评论集中在很短的时间内,随后评论数减少,因此信息的消亡速度快。
(2)本文建立了基于用户活跃时间异质性的信息传播模型。对建立的模型进行数值仿真和理论分析,得到的结论有：一、群体层面,用户活跃时间间隔和用户转发信息时间间隔都服从幂律分布,并且两者的幂指数相关；二、用户活跃时间间隔分布幂指数越小,即时间间隔异质性越大,导致信息传播速度越慢,感染用户比例也越小；
(3)为了理解社交网络的形成及演化机制,本文在连接最近邻(CNN)机制和强度驱动(SD)机制的基础上,提出了基于强度驱动的连接最近令(?)(SD-CNN)模型。该模型不仅反映了社交网络形成的实际情况：两个具有共同好友的用户成为好友的可能性比随机选取的两个用户大的多,同时又考虑了用户选择的偏好性。对模型进行数值仿真,得到的结论有：生成的网络具有服从幂律分布的节点度和节点强度,并且节点度和节点强度呈正的非线性相关关系。
(4)定量研究了具有社团结构网络上的信息传播动力学过程,并建立了信息传播模型。模型的仿真结果揭示：社团结构间的链接数对信息传播的速度和范围具有决定性影响。当网络规模一定时,网络划分的社团结构数对信息传播过程也有重要影响。本章的研究结果为信息传播的优化和控制提供了建议。
With the popularity of Web2.0and the fast development of related technologies, the Social Networking Service as a form of network application has developed rapidly. Nowadays, the social network has become an important platform for people to disseminate information, express views and interact with each other. Different from the traditional ways of information dissemination, user activity patterns and social network structure characteristics make the propagation of information becoming more complex and uncertain, and traditional mathematical model of spreading dynamics can't depict the spreading phenomenon in online social network. In view of this, combined with the theories and methods of the disciplines of information science, human dynamics, complex network and spreading dynamics, we empirical analysis and modeling of information spreading in social network. In this thesis, we study the impact of human activity patterns on information propagation, structural characteristics of social networks and evolutionary mechanismes. Furthermore, we investigate the impact of the structural characteristics of social networks on the information propagation. Empirical results and the findings are consistent and have great value scientific research and application.
The main contents and conclusions are as follows:
(1) We study the impact of bursty human comment patterns on the popularity of online content. In this paper, we first analyses the dataset from Sina micro-blogging. The result suggests that users comment patterns have periodic and bursty. The popularity of micro-blogging (namely the number of comments of micro-blogging) diminishes over time with power-law. According to the users comment patterns, we establish the model of information propagation and give the theoretical analysis. The analytical result shows that the user comment patterns are closely related to micro-blogging popularity. Finally, we comparative analyze the theoretical results and empirical results, and they are all support the conclusion very well.
(2) We investigate the impact of the temporal heterogeneity on the information propagation. Empirical studies have shown that human life rhythms and patterns of activity greatly affect the information dissemination, especially temporal heterogeneity. In addition, the prevalence of online social networking services makes users inundated with a lot of information. Based on these two factors we establish the model of information propagation. From the simulation results, we find that the propagation velocity increases monotonously with the increase of temporal heterogeneity exponent. Furthermore, the decay of propagation velocity, namely the newly infected individuals at each time step, also follows power law distribution. Meanwhile, the exponent characterizing the temporal heterogeneity is related to that in the decay of propagation velocity by the relationβ=α-1. These results are well supported by both the theoretical predictions and empirical data.
(3) In order to understand the mechanism of the formation and evolution of social network, we propose the SD-CNN model. The model based on the mechanism of strength driven attachment and connecting the nearest neighbor, and reflects the real phenomenon of social network:two users who have common friends become friends is more likely than randomly selected two users. Simulation results show that the degree and strength of nodes follow power law distributions.
(4) We investigate the impact of community structure on information propagation on social network. We established the propagation model not only consider the number of community structure, but also the number of links between the communities. The conclusions are as follows:for a piece of information propagation on social networks, we find that the number of links among communities determines the fraction of infected nodes. And the number of community structure also has great impact on information propagation process. The results can be useful for optimizing or controlling information, such as rumor or facts, propagate on online social networks.
论文主要的研究内容和结论包括以下四个方面：
(1)研究了新浪微博用户的爆发性评论方式对微博信息流行度的影响。本文对新浪微博的真实网络数据统计研究发现,新浪微博在线用户评论信息的行为具有异质性,表现在两次连续的评论时间间隔服从幂律分布。因此本文基于新浪微博用户评论行为异质性的假设,对微博信息流行度消亡行为进行建模,并给出理论分析。得到的主要结论：当用户评论时间间隔幂指数越大,则评论爆发性越强,大部分评论集中在很短的时间内,随后评论数减少,因此信息的消亡速度快。
(2)本文建立了基于用户活跃时间异质性的信息传播模型。对建立的模型进行数值仿真和理论分析,得到的结论有：一、群体层面,用户活跃时间间隔和用户转发信息时间间隔都服从幂律分布,并且两者的幂指数相关；二、用户活跃时间间隔分布幂指数越小,即时间间隔异质性越大,导致信息传播速度越慢,感染用户比例也越小；
(3)为了理解社交网络的形成及演化机制,本文在连接最近邻(CNN)机制和强度驱动(SD)机制的基础上,提出了基于强度驱动的连接最近令(?)(SD-CNN)模型。该模型不仅反映了社交网络形成的实际情况：两个具有共同好友的用户成为好友的可能性比随机选取的两个用户大的多,同时又考虑了用户选择的偏好性。对模型进行数值仿真,得到的结论有：生成的网络具有服从幂律分布的节点度和节点强度,并且节点度和节点强度呈正的非线性相关关系。
(4)定量研究了具有社团结构网络上的信息传播动力学过程,并建立了信息传播模型。模型的仿真结果揭示：社团结构间的链接数对信息传播的速度和范围具有决定性影响。当网络规模一定时,网络划分的社团结构数对信息传播过程也有重要影响。本章的研究结果为信息传播的优化和控制提供了建议。
With the popularity of Web2.0and the fast development of related technologies, the Social Networking Service as a form of network application has developed rapidly. Nowadays, the social network has become an important platform for people to disseminate information, express views and interact with each other. Different from the traditional ways of information dissemination, user activity patterns and social network structure characteristics make the propagation of information becoming more complex and uncertain, and traditional mathematical model of spreading dynamics can't depict the spreading phenomenon in online social network. In view of this, combined with the theories and methods of the disciplines of information science, human dynamics, complex network and spreading dynamics, we empirical analysis and modeling of information spreading in social network. In this thesis, we study the impact of human activity patterns on information propagation, structural characteristics of social networks and evolutionary mechanismes. Furthermore, we investigate the impact of the structural characteristics of social networks on the information propagation. Empirical results and the findings are consistent and have great value scientific research and application.
The main contents and conclusions are as follows:
(1) We study the impact of bursty human comment patterns on the popularity of online content. In this paper, we first analyses the dataset from Sina micro-blogging. The result suggests that users comment patterns have periodic and bursty. The popularity of micro-blogging (namely the number of comments of micro-blogging) diminishes over time with power-law. According to the users comment patterns, we establish the model of information propagation and give the theoretical analysis. The analytical result shows that the user comment patterns are closely related to micro-blogging popularity. Finally, we comparative analyze the theoretical results and empirical results, and they are all support the conclusion very well.
(2) We investigate the impact of the temporal heterogeneity on the information propagation. Empirical studies have shown that human life rhythms and patterns of activity greatly affect the information dissemination, especially temporal heterogeneity. In addition, the prevalence of online social networking services makes users inundated with a lot of information. Based on these two factors we establish the model of information propagation. From the simulation results, we find that the propagation velocity increases monotonously with the increase of temporal heterogeneity exponent. Furthermore, the decay of propagation velocity, namely the newly infected individuals at each time step, also follows power law distribution. Meanwhile, the exponent characterizing the temporal heterogeneity is related to that in the decay of propagation velocity by the relationβ=α-1. These results are well supported by both the theoretical predictions and empirical data.
(3) In order to understand the mechanism of the formation and evolution of social network, we propose the SD-CNN model. The model based on the mechanism of strength driven attachment and connecting the nearest neighbor, and reflects the real phenomenon of social network:two users who have common friends become friends is more likely than randomly selected two users. Simulation results show that the degree and strength of nodes follow power law distributions.
(4) We investigate the impact of community structure on information propagation on social network. We established the propagation model not only consider the number of community structure, but also the number of links between the communities. The conclusions are as follows:for a piece of information propagation on social networks, we find that the number of links among communities determines the fraction of infected nodes. And the number of community structure also has great impact on information propagation process. The results can be useful for optimizing or controlling information, such as rumor or facts, propagate on online social networks.
引文
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