论文部分内容阅读
微博网络的快速性、爆发性和时效性,以及用户复杂的行为模式,使得研究其信息传播模型及影响因素成为网络舆情的热点方向.利用压缩映射定理,分析不动点迭代过程的收敛条件,得到有向网络信息传播过程的渗流阈值和巨出向分支的数值解法;通过可变同配系数生成模型,分析关联特征对信息传播的影响;最后利用微博转发网络数据进行仿真对比实验.结果表明:虽然四类关联特征同时体现出同配、异配特征,但信息传播结果更多受入度-入度相关性、入度-出度相关性影响;通过删除少量节点的方法,提取边同配比例,验证大部分节点的四类关联特征呈现一致性.
The rapidity, explosiveness and timeliness of the microblogging network, as well as the complex behavior patterns of the users make it possible to study the information dissemination model and its influencing factors as the hot spots in the network public opinion.We use the contraction mapping theorem to analyze the convergence condition of the fixed point iteration process , We obtain the numerical solution of the seepage threshold and the giant outbound branch of the process of directed network information propagation. Through the generation of a model with variable co-ordination coefficients, we analyze the influence of the correlation features on the information dissemination. Finally, we use microblog to forward the network data for simulation comparison. The results show that although the four types of correlation features show the same-match and different-match characteristics at the same time, the results of information dissemination are more affected by the correlation of entry-entry degree and entry-exit degree. By removing a few nodes, With proportion, verify that the four types of correlation features of most nodes are consistent.