基于主动异构监督的重叠社区发现及其模型选择方法研究

项目名称： 基于主动异构监督的重叠社区发现及其模型选择方法研究

项目编号： No.61503281

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 杨亮

作者单位： 河北工业大学

项目金额： 20万元

中文摘要： 复杂网络中社区结构的检测对理解网络功能有着十分重要的意义,被广泛用于恐怖组织识别、社交网络分析等实际问题。但随着网络结构的愈加复杂，单纯依靠拓扑信息的社区检测很难获得令人满意的效果。近年来，已提出了一些通过融合各类背景信息的半监督社团发现方法，但这些方法在效率、适用范围和自动确定社区个数等方面存在较大的不足，故而限制了他们的实际应用。为了使半监督社区发现方法在现实场景中有着更广的适用范围和更好的性能，项目拟从如何提升半监督社区发现的高效性、适应性和自动性等三个方面开展研究，旨在开发能够高效利用异构监督信息、在具有重叠社区结构的网络中进行自动化（同时检测社区结构和确定社区个数）精确社区发现的算法。项目主要包括：1）从被动高效利用和主动有选择性的获取监督信息两个方面来提升效率；2）从适应多类异构监督信息和多类复杂社区模式两个角度提升适应性；3）设计同时检测社区结构和社区个数的算法来提升自动性

中文关键词： 社区发现；图模式挖掘

英文摘要： Community detection in complex networks is of great significant importance for comprehending network functions, which has been widely used in many areas, such as terrorist organization recognition, social network analysis, etc. As network structures become complicated, approaches based solely on network topology cannot yield satisfactory results. Thus, some semi-supervised community detection algorithms have been proposed to alleviate this problem in the last few years. Most of them, however, are deficient in efficiency, scope of application and determination the number of communities, which limits their practical application. To make semi-supervised community detection can be applicable to more real areas and achieve better performance, this proposal conducts research on how to improve their efficiency, applicability and automaticity, i.e., detect the community structures and determine the number of communities simultaneously. We aim to develop a group of algorithms which can efficiently make use of heterogeneous supervised information and automatically detect the overlapping community structures. The proposal consists of three components. 1) To improve the efficiency, we carry out research on how to efficiently make use of obtained supervised information and how to actively select the most useful component for human labeling. 2) To improve the applicability, we conduct research on how to simultaneously integrate heterogeneous supervised information and how to design algorithm that can be used to networks with many kinds of community structures, e.g., overlapping community structure. 3) To improve the automaticity, we will design a novel semi-supervised community detection algorithm that can simultaneously detect the community structures and determine the number of communities.

英文关键词： community detection;graph mining