基于群体一致性动力学的网络社团结构识别研究

项目名称： 基于群体一致性动力学的网络社团结构识别研究

项目编号： No.61203032

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： 杨波

作者单位： 武汉理工大学

项目金额： 24万元

中文摘要： 群体系统是由大量相互作用的自主或半自主子系统通过网络互联所构成的复杂系统。移动机器人群、传感器网络甚至社会网络都是典型实例。群体的复杂网络拓扑结构强烈地支配着系统的组织与功能。本项目基于上述事实做逆向考量，将群体一致性动力学作为群体网络社团结构的探测工具，提出复杂网络社团结构识别的动力学理论框架，克服传统模块函数方法存在的固有缺陷，揭示动力学与网络拓扑间的内在深刻联系。研究内容主要包括1）空间变换下的聚类分析方法：对群体一致性动力学行为的时间序列数据进行信息抽取，将复杂网络中的社团识别问题变换为欧氏空间中的向量聚类分析问题，应用k近邻密度与核函数方法识别网络拓扑的社团结构；2）基于领导者的分布计算方法：运用影响力分析，在节点邻域层次上辨识网络社团的骨架结构，进而运用群体一致性动力学作为分布式计算工具渐近地探明网络社团的微观结构。研究结果对群体系统与网络理论及其工程实践的发展具有重要意义。

中文关键词： 群体系统；一致性；社团识别；动力学；复杂网络

英文摘要： A swarm is a complex system composed of a large number of interacting autonomous or semiautonomous subsystems interconnected by networks. Swarms of mobile robots, sensor networks, and even social networks are typical instances. Research during the past decade showed that the complex network topologies intensively govern the organizations and functions of the swarms. This evidence suggests inverse approaches to the problem in which the consensus dynamics is used as a probe for the understanding of the community structure of networks. We propose a dynamical theoretical framework for the community detection, with an emphasis on tackling the inherent flaws of the traditional modularity function method. Our dynamical approaches clarify the deep connections between the dynamics and the network topology. The proposed research mainly involves 1) cluster analysis method under spatial transformation, and 2) distributed computing method based on network leaders. For the first theme, we extract the information from the time series data by observing the dynamical behaviors of the swarm consensus, then transform the community detection problems into the vector cluster analysis problems in the Euclidean space, and finally detect the community structure by the methods of the k-nearest neighbor density and kernel function. For t

英文关键词： Swarm；Consensus；Community detection；Dynamics；Complex networks