基于复杂网络的IC-SEIR网络舆情传播模型研究

基于传染病模型进行舆情传播的研究中，已有传播模型中节点的状态转化仅取决于设定的概率，并不符合真实的传播情况。根据现实中用户传播舆情信息的行为特征，将兴趣度与亲密度引入舆情传播的过程中，并扩展了SEIR模型中的状态转移途径，提出了IC-SEIR网络舆情传播模型，使用兴趣度、相似度来描述节点的相似性，亲密度来描述节点间连接的紧密程度，并且节点的状态转移方向取决于这两种属性。在Matlab平台下采用Facebook数据集进行仿真分析，仿真结果表明，当初始传播点为度大节点或兴趣度分布为均匀分布或常数时，舆情信息更容易传播，当亲密度分布为常数时对舆情传播也有促进作用，为进一步研究社会网络中舆情传播的过程与趋势提供参考。

Research on IC-SEIR Public Opinion Propagation Model Based on Complex Network

In the study of the use of epidemic model for public opinion propagation,the transition of node states depends only on the set probability and does not fit with the real propagation situation.The IC-SEIR public opinion propagation model is proposed.Then,according to the behavior characteristics of the user''s propagation of public opinion in reality,the interest and closeness attributes are introduced into the public opinion propagation process,and the state transition path in the SEIR model is extended.The interested attribute is used to describe the similarity of nodes and the closeness attribute is used to describe the tightness of connections between nodes,and the state transition direction of the nodes depends on these two attributes.The simulation is carried out on the Matlab platform using the Facebook dataset.The results show that the public opinion propagates faster and wider when the initial infected nodes with large degree or the distribution of interest is constant or uniform distribution,and the constant distribution of closeness also plays a role in promoting propagation of public opinion.The research results can provide reference for the study of public opinion propagation in social network.