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基于复杂网络的信息传播规律研究
Alternative TitleResearch on the pattern of Information Dissemination Based on Complex Networks
杨焕成1,2
Department数字工厂研究室
Thesis Advisor郑泽宇
ClassificationG206
Keyword社交网络 信息传播 网络结构 被转发概率 社区检测
Call NumberG206/Y27/2018
Pages63页
Degree Discipline机械制造及其自动化
Degree Name硕士
2018-05-17
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract本文将基于社交网络本身的无尺度特性,以Twitter网用户之间的好友关系拓扑图为研究内容,采用SIR传播模型进行信息传播实验,通过对比不同因素变化对信息传播范围的影响,探究在线社交网络中信息传播机制以及影响传播效果的重要因素。通过确定消息传播过程中的突变,文章发现消息内容比发送者的人气更加能够影响消息的传播。另外,消息被转发的概率存在一个影响到消息传播能力的阈值。当消息被转发的概率在阈值之上时就很容易得到大规模传播。之后我们得到了在网络中信息得以广泛传播的阈值,以此可判断且使得消息得到大范围传播。此外,网络的社区结构特性可以使得信息在局部地区更容易传播、不同社区结构之间可以通过连接用户进行传播。充分利用网络的社区结构将使得信息的传播更加有效。论文中,通过将传染病传播模型应用于推特网的实名数据,通过随机游走试验的方式得到了一个可以在实际操作中确定的传播阈值,而不再是模型中难以测量的参数。此外,本文还拓展了网络中计算子核核度的方法,以此来快速得到网络社区检测的结果,并将结果用于增强信息传播效果。
Other AbstractBy determining the mutation in the process of message dissemination, the article finds that the content of the message is more likely to affect the spread of the message than the sender's popularity. We also find that the probability that a message will be forwarded has a threshold that affects its ability to spread, and when the probability is above the threshold the message quickly achieves mass dissemination. Thresholds are then obtained, which can be used to judge and make the message widely spread. In addition, the characteristics of the community structure of the network can make information easier to spread in local areas, and different community structures can be spread by connecting users with big betweenness. Making full use of the network's community structure will make the dissemination of information more effective. This paper applies the infectious disease propagation model(SIR) to the real-name data of Twitter and obtains a propagation threshold that can be determined by the random-walk. It is no longer a difficult parameter to measure in the model. In addition, this paper also extends the method of calculating the nuclear score in the network, so as to quickly obtain the results of network community detection, and the results used to enhance the effect of information dissemination.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/21798
Collection数字工厂研究室
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
杨焕成. 基于复杂网络的信息传播规律研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.
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