Abstract:

Retransmission is the momentous way for information dissemination on the social network. Researching user’s behavior patterns will help monitor and dominate the propagation of topics on the internet. In the current research, the existing models generally lack timeliness, and user’s behaviors are difficult to describe accurately. Therefore, this paper analyzes the properties of the current social networks and improves the mathematic model. Firstly, the paper improves the retransmission probability calculation based on the user connection strength and the influence of neighbor nodes. Secondly, among the classic infectious disease dynamics SCIR model, the paper introduces online and offline state of nodes, through the user online ratio control network activity. The simulation results show that the proposed model has better stability and coverage than the traditional SCIR model in the information dissemination process. The nodes variety trend gets closer to the real network, which can better simulate the propagation of hot topics in social networks.

Key words: information dissemination, social network, customer behavior, infectious disease

摘要：

社交网络中用户转发是信息传播的重要渠道，研究用户转发模式和信息传播规律，将有利于在网络话题传播过程中进行监控和抑制。现有的建模研究中，存在模型通常缺少时效性，用户行为难以准确刻画的问题。因此，着重分析了社交网络用户行为模式，基于用户连接强度和邻居节点的影响改进了转发概率计算，其次在经典的传染病动力学SCIR模型中，引入在线和离线状态的节点，通过用户在线比率控制网络活跃度。仿真结果表明，该模型相较传统SCIR模型在信息传播过程中具有较好的稳定性和更高的覆盖率，节点属性变化走势更加接近真实网络，可以较好地模拟社交网络中的热点话题的传播规律。

关键词: 信息传播, 社交网络, 用户行为, 传染病模型