In complex network research, the infectious disease model is often used to study transmission mechanisms and interference factors of information; consequently, they are essential for the prediction and control of information transmission. The conventional SIRS epidemic model has a wide range of applications and is theoretically mature. However, it does not stratify the nodes in a network, and fails to reflect the characteristics of different nodes. To solve this problem, we propose a layered SIRS information transmission model (L-SIRS) . Depending on the node influence, we assign the nodes in the network to high- and low-influence layers and establish an intra- and inter-layer information transmission mechanism. The transmission threshold and equilibrium point of this model are analyzed theoretically. To reduce the transmission of online public opinions, two kinds of information transmission interference strategies, namely, information blocking and information dredging, are designed to study their influence on information transmission. Finally, combine with practice, the simulation results indicate that the L-SIRS model can more accurately describe network information transmission and both information blocking and information dredging can effectively inhibit the spread of information.

在复杂网络研究中，传染病模型常用于研究信息的传播机制和干扰因素；因此，它们对于信息传输的预测和控制至关重要。传统的SIRS流行模型应用范围广，理论上已经成熟。但是，它没有对网络中的节点进行分层，也不能体现不同节点的特性。为了解决这个问题，我们提出了一种分层的SIRS信息传输模型（L-SIRS）。根据节点影响，我们将网络中的节点分配到高影响层和低影响层，并建立层内和层间信息传输机制。对该模型的传输阈值和平衡点进行了理论分析。为减少网络舆论传播，设计了信息阻断和信息疏导两种信息传播干扰策略，研究其对信息传播的影响。最后，结合实践，仿真结果表明，L-SIRS模型能够更准确地描述网络信息传输，信息阻塞和信息疏浚都能有效抑制信息的传播。