基于语义路径的异质网络社区发现方法
发布时间：2018-11-25 11:31
【摘要】：社区发现是社会网络研究的热点问题,综合利用社会网络中不同对象间的异质信息,可以更加有效地挖掘网络中的社区结构.针对传统的社区发现方法无法有效地利用异质信息的问题,本文提出了一种基于语义路径的异质网络社区发现方法,该方法首先定义网络中的语义路径,通过语义路径来衡量不同类型对象间的异质信息相似度,然后以此构造可靠性矩阵,作为半监督非负矩阵分解的正则化约束项,进而实现异质网络的社区划分.在真实数据集上的实验结果表明,所提出的方法能够更准确地发现异质网络中的社区结构.
[Abstract]:Community discovery is a hot topic in social network research. Using heterogeneous information among different objects in social network can effectively excavate the community structure in social network. In order to solve the problem that traditional community discovery methods can not effectively utilize heterogeneous information, this paper proposes a semantic path-based heterogeneous network community discovery method, which first defines semantic paths in the network. The similarity of heterogeneous information between different types of objects is measured by semantic paths, and then the reliability matrix is constructed as a regularization constraint for semi-supervised non-negative matrix decomposition, and then the community partition of heterogeneous networks is realized. Experimental results on real data sets show that the proposed method can more accurately detect the community structure in heterogeneous networks.
【作者单位】： 国家数字交换系统工程技术研究中心;
【基金】：国家科技支撑计划(No.2014BAH30B01)
【分类号】：TP393.09
，
本文编号：2355951
[Abstract]:Community discovery is a hot topic in social network research. Using heterogeneous information among different objects in social network can effectively excavate the community structure in social network. In order to solve the problem that traditional community discovery methods can not effectively utilize heterogeneous information, this paper proposes a semantic path-based heterogeneous network community discovery method, which first defines semantic paths in the network. The similarity of heterogeneous information between different types of objects is measured by semantic paths, and then the reliability matrix is constructed as a regularization constraint for semi-supervised non-negative matrix decomposition, and then the community partition of heterogeneous networks is realized. Experimental results on real data sets show that the proposed method can more accurately detect the community structure in heterogeneous networks.
【作者单位】： 国家数字交换系统工程技术研究中心;
【基金】：国家科技支撑计划(No.2014BAH30B01)
【分类号】：TP393.09
，
本文编号：2355951
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