链接推荐用于增强社交网络信息扩散方法研究

发布时间：2018-03-03 20:30

  本文选题：链接推荐　切入点：社区发现　出处：《哈尔滨工业大学》2014年硕士论文　论文类型：学位论文

【摘要】：以往关于链接推荐方面的研究，大部分关注于对社会交往功能的加强，而忽视了对信息扩散功能的加强。链接推荐算法不应该仅仅专注于评估用户信息间的相似度或者社交关系间的相似度，同时也应该关注那些应用了推荐算法在网络中增加的边，使得信息在更新的网络结构上获得扩散的最大化。前人通过引入社区发现提出了结点扩散度的概念，，并将结点扩散度的结果和传统链接预测结果相结合来解决上述问题。 本文的研究工作主要分为以下三方面： 1.首先给出了链接推荐用于增强信息扩散的研究框架，并选定了框架中核心部分相对应的算法和模型，对比分析了现有的三种计算结点扩散度的方法； 2.分析现有结点扩散度算法的不足，通过引入社区对结构提出了非重叠社区结点扩散度改进版算法； 3.分析重叠社区的信息扩散结构，通过引入结点的社区中心性提出了重叠社区的结点扩散度算法。 本文在真实的无向社交网络数据集Email-Enron及Amazon和有向社交网络数据集Email-EuAll上进行了对比实验，结果在紧密型网络上超过了现有方法对信息扩散的促进。 本文更加注重结点的社区属性对信息扩散的影响，完善了结点扩散度解决方案体系，大幅度降低了计算复杂度，使得可以适用于大规模社交网络，另外本文还通过实验引出了两个值得关注的问题，有助于后人对结点扩散度方法体系的进一步研究。
[Abstract]:Most of the previous studies on link recommendations have focused on strengthening social interaction, Link recommendation algorithms should not only focus on evaluating the similarity between users' information or social relationships, but also on the edges that are added to the network by using the recommendation algorithm. By introducing community discovery, the concept of node diffusivity is put forward, and the results of node diffusion are combined with the traditional link prediction results to solve the above problems. The research work of this paper is divided into the following three aspects:. 1. Firstly, the research framework of link recommendation to enhance information diffusion is given, and the corresponding algorithms and models of the core parts of the framework are selected, and three existing methods to calculate the diffusion of nodes are compared and analyzed. 2. Analyzing the deficiency of the existing node diffusivity algorithm, an improved algorithm is proposed by introducing community to the structure of non-overlapping community node diffusivity. 3. The information diffusion structure of overlapping communities is analyzed, and the algorithm of node diffusion degree is proposed by introducing the community centrality of nodes. This paper makes a comparative experiment on the real undirected social network data set Email-Enron and Amazon and the directed social network data set Email-EuAll. The results show that the information diffusion on the compact network is more than that of the existing methods. This paper pays more attention to the effect of the community attribute of the node on the information diffusion, improves the solution system of node diffusion, greatly reduces the computational complexity, and makes it suitable for large-scale social networks. In addition, this paper raises two problems worth paying attention to through experiments, which is helpful to the further study of the method system of node diffusivity.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.0

【参考文献】

中国期刊全文数据库 前3条

1 骆志刚;丁凡;蒋晓舟;石金龙;;复杂网络社团发现算法研究新进展[J];国防科技大学学报;2011年01期

2 许晓东;肖银涛;朱士瑞;;微博社区的谣言传播仿真研究[J];计算机工程;2011年10期

3 李栋;徐志明;李生;刘挺;王秀文;;在线社会网络中信息扩散[J];计算机学报;2014年01期

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