摘要
针对当前复杂网络重叠社区发现的热点问题,提出基于二阶段聚类的重叠社区发现算法.对网络邻接矩阵进行特征分解时,节点投影到k维欧氏空间后,对节点先后进行硬聚类和软聚类,高效自适应地挖掘网络中的重叠社区结构.在硬聚类阶段中,引入基于距离最小原则的一趟聚类算法对节点进行自适应的硬划分,确定软聚类阶段中的聚类中心和网络的社区数量.在软聚类阶段中,引入以模糊模块度为目标函数的模糊C均值算法,通过迭代优化模糊模块度实现对节点的软划分,挖掘网络中的重叠社区结构.在多个真实网络数据集上的实验验证文中算法能高效挖掘复杂网络中的重叠社区结构.
Aiming at the complex network overlapping community detection, an overlapping community detection algorithm based on two-stage clustering is proposed. Eigen decomposition is applied to network adjacency matrix. The nodes are projected into k-dimensional Euclidean space, and then they are clustered by hard and soft clustering algorithm to detect the structure of overlapping community efficiently and adaptively. At the stage of hard clustering, a clustering algorithm based on the principle of minimum distance is introduced to divide nodes autonomously, and the number of communities and cluster centers for the soft clustering stage are determined. At the stage of soft clustering, fuzzy C-means algorithm is introduced and the fuzzy modularity is considered as objective function for the algorithm. Through iterative optimization of the fuzzy modularity, a soft partition is realized and overlapping community structures in network can be figured out. Experiments are carried out on a number of real network datasets, and the results indicate that the proposed algorithm can mine overlapping community structure in complex network with high efficiency and effectiveness.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2015年第11期983-991,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61572145)
教育部人文社会科学研究青年项目(No.14YJC870021)
广东省科技计划项目(No.2015A030401093,2014A04041083)资助
关键词
重叠社区
社区发现
模糊聚类
模糊模块度
特征分解
Overlapping Community, Community Detection, Fuzzy Clustering, Fuzzy Modularity,Eigen Decomposition
