结点—链接协同划分的复杂网络重叠社团发现方法研究

项目名称： 结点—链接协同划分的复杂网络重叠社团发现方法研究

项目编号： No.61303110

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 金弟

作者单位： 天津大学

项目金额： 26万元

中文摘要： 复杂网络社团发现对理解网络功能、预测网络行为等有重要意义，被广泛应用于恐怖组织识别、社交网络分析等实际问题，重叠社团发现是其研究热点。目前已提出一些不同类型的重叠社团发现方法，其中2010年Nature上报道的链接划分思想，由于概念的自然性，被视为一类特别有前景的方法。然而这类方法仅适用于社团结构高度重叠的网络。针对"如何有效发现任意重叠程度之社团结构"这一难题，本项目拟从结点-链接协同划分的角度开展研究。通过将网络划分为由结点社团和链接社团共同组成的混合社团结构，开发一类新的重叠社团发现方法。该思路不仅允许结点同属于多个社团，而且不强制任何一条边都属于某个社团，因此可天然描述具有任意重叠程度的网络社团结构。本项目将重点研究基于统计模型的结点-链接协同划分方法，主要包括：1）同时刻画结点社团和链接社团的统一生成模型；2）统一生成模型的参数学习方法；3）最优结点-链接混合社团结构的选择策略。

中文关键词： 复杂网络聚类；重叠社团发现；统计模型方法；；

英文摘要： Community detection in complex networks is of fundamental importance for comprehending network function and forecasting network activities, which has been used in many areas, such as terrorist organization recognition, social network analysis, etc. Especially, the detection of overlapping communities is the current research focus in this area. Recently, there are several types of overlapping community detection methods having been proposed. The link partitioning method, as the conceptually naturality, is a particularly promising class of techniques for this task, being actively researched and developed. However, this type of method tends to get highly overlapped communities, which is not well-suitable for networks with slightly overlapped community structures. For identifying the community structures with arbitrary varying degrees of overlaps, the proposal here attends to study the problem of overlapping community detection from the perspective of hybrid node-link partitioning. Our purpose is to develop a new class of method for the detection of overlapping communities by dividing a network into a hybrid node-link community structure, which consists of both node communities and link communities as its elements. This idea not only permits nodes to belong to multiple communities, but also does not force every link

英文关键词： Complex Network Clustering；Overlapping Community Detection；Probabilistic Model Method；；