有向在线社交网络中信息传播监测节点的选择

发布时间：2018-01-03 02:25

本文关键词：有向在线社交网络中信息传播监测节点的选择　出处：《小型微型计算机系统》2014年02期 　论文类型：期刊论文

【摘要】：网络传播在病毒营销、谣言扩散、舆情控制等多个领域中都具有重要的研究价值.不同于传统的最大化传播研究,本文的研究目的是如何在网络中选择有效的监测节点,以获得更大的监测效果.基于独立级联模型,通过在有向网络中模拟起源于不同节点的传播,能够获得各节点监测能力的仿真结果.在对两个真实的有向社交网络的试验中发现,相比较于节点的K-core索引值和出度值,节点的监测能力更依赖于其D-core索引值.进而,本文通过结合D-core索引值和节点出度值提出了一种节点监测中心Mc的计算方法,与PageRank、HIT等传统的有向图中心性算法相比,监测中心Mc值可以更好地反应节点的监测能力.
[Abstract]:Internet communication has important research value in many fields, such as virus marketing, rumor spread, public opinion control and so on. It is different from the traditional maximization communication research. The purpose of this paper is how to select the effective monitoring nodes in the network to obtain greater monitoring effect. Based on the independent cascade model, the propagation originating from different nodes is simulated in the directed network. In the experiment of two real directed social networks, it is found that the K-core index value and the outlier value are compared with each node. The monitoring ability of nodes depends more on their D-core index value. Furthermore, this paper proposes a calculation method of node monitoring center Mc by combining D-core index value and node outlier value. Compared with the traditional directed graph centrality algorithm such as PageRankan hit, the Mc value of the monitoring center can better reflect the monitoring ability of nodes.
【作者单位】：合肥电子工程学院网络系;国防科学与技术大学计算机学院;
【基金】：国家自然科学基金项目(61202337)资助
【分类号】：TP393.09;G206
【正文快照】： 1引言当前,随着人们越来越多的依赖于在线社交网络(OnlineSocial Networks,OSNs)进行信息传播与交流,使得在大规模OSNs中挖掘信息传播规律、传播模型和预测传播结果成为近年来的研究热点.OSNs一方面为研究者提供了真实的社会关系网络结构[1],另一方面也提供了大量的传播实例[2

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