异质网络中的社区发现

项目名称： 异质网络中的社区发现

项目编号： No.61203154

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 刘欣

作者单位： 武汉理工大学

项目金额： 23万元

中文摘要： 根据给定网络的连接结构，将节点划分为若干组，使得各组节点分别对应于某一功能单元，以上过程称为社区发现。近年来，社区发现受到很多学者的关注，他们往往将此问题限定于同质网络。现实中，由不同类节点和边构成的异质网络以多种形式广泛存在，而同质网络的社区发现算法无法适用于更为复杂的异质网络。本项目在同质网络的最优化、信息论、谱分析、统计推断等理论的扩展和延伸的基础上，引入分而治之、整体规划、等价转化和函数优化四个思路来建立算法框架，对形形色色异质网络中的社区发现展开系统的研究，以揭示异质网络结构和功能之间的关系，为现实复杂异质系统的结构分析、未知功能探测和知识发现提供有效的方法和途径。本课题的预期研究成果在Web信息搜索、网站用户行为分析、定向广告、个性化服务等方面具有广泛的应用前景。

中文关键词： 社区发现；社团分析；模块度；异质网络；属性网络

英文摘要： Many complex systems can be described as networks, where the nodes represent the fundamental entities of a system and the edges represent relationships or interactions between them. The study of networks has a long history and proves great success in understanding the structures and dynamics of complex systems. A prominent problem in studying networks is community detection, i.e. the detection of groups of nodes which share common properties and/or play similar roles known as communities. Previous research on community detection overwhelmingly focuses on homogeneous networks. That is, only one type of nodes is present in a network, and the edges between nodes are of the same type. In real-world systems, however, there are often more than one type of entities and different types of interactions between them, leading to the prevalence of heterogeneous networks. The goal of this research project is to provide a principled generalization of community detection to heterogeneous networks. The approach is based on extensions of techniques that were previously developed for network science, such as spectral analysis, optimization theory, information theory, and statistical inference. The research covers the following key topics: 1) The definition of a community based on empirical study and computer simulation; 2) The s

英文关键词： community detection；community structure；modularity；heterogeneous network；attributed network