[1]万新贵,李玲娟. 基于结构与属性的社区划分方法[J].计算机技术与发展,2017,27(08):97-101.
　WAN Xin-gui,LI Ling-juan. Community Division Method with Structure and Attribute[J].,2017,27(08):97-101.
点击复制

 基于结构与属性的社区划分方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年08期
页码:
97-101
栏目:
智能、算法、系统工程
出版日期:
2017-08-10

文章信息/Info

Title:
 Community Division Method with Structure and Attribute
文章编号:
1673-629X（2017)08-0097-05
作者:
 万新贵; 李玲娟
 南京邮电大学 计算机学院
Author(s):
 WAN Xin-gui; LI Ling-juan
关键词:
 ; 社区划分; 度; K-means; 中心点; 欧氏距离
Keywords:
 ; community division; degree; K-means; center; Euclidean distance
分类号:
TP301
文献标志码:
A
摘要:
 目前通行的社区划分方法大多基于结构,但单纯基于结构的划分不能挖掘出社区对象的潜在关系,因而不能发现社区的变化趋势.为此,提出了基于结构的社区划分算法(Community Division based on Structure,CDS).该算法利用度和节点欧氏距离对社会网络进行结构划分;同时针对经典K-means算法在社区划分中所存在的随机选取初始中心点以及k值选取不合理所导致的聚类结果不佳问题,提出了一种基于社区结构的非人为设定k值的K-means算法-NPCluster(Non Presetting Cluster)算法.该算法基于由CDS算法所提到的社区结构,依次选取度最大的节点作为聚类中心点,以小于平均特征欧氏距离为基准合并簇集,反复迭代直至聚类完成.理论分析和对比实验结果表明,CDS算法能够有效划分出社区结构;相对于K-means算法,NPCluster算法在已划分的社区结构上具有更高的聚类精度和更好的时效性;结构与属性相结合的社区划分方法是有效可行的.
Abstract:
 Most of the current methods of community division are based on structure,but the structure-based division cannot excavate the potential relationship of community objects,which is not to find the tendencies of community variations.Therefore a community-based partitioning algorithm (Community Division based on Structure,CDS) has been designed which applies degree and node-Euclidean distance to divide social network.Simultaneously,an algorithm by nonhuman (artificial) setting k-value-NPCluster algorithm (Non Presetting Cluster)-based on the community structure has been proposed,which is based on the community structures divided by CDS algorithm and has improved the unsatisfactory clustering outcomes caused by the inappropriateness of random selection of initial centers and that of K value.Thus the maximum degree nodes are chosen as a cluster center in turn and the data are merged and clustered until the average feature-Euclidean distance is less than a given threshold.Theoretical analyses and experimental results show that the proposed CDS algorithm can effectively divide the community structures;compared with K-means algorithm,NPCluster algorithm has higher clustering quality and better clustering timeliness on the divided community;the community division method based on structure and attribute is practical and effective.

相似文献/References:

[1]王硕 王新华 刘婧 刘永.一种基于社区划分的数据分发方法[J].计算机技术与发展,2011,(10):210.
　WANG Shuo,WANG Xin-hua,LIU Jing,et al.A Data Forwarding Method Based on Community Division[J].,2011,(08):210.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(08):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(08):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(08):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(08):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(08):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(08):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(08):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(08):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(08):43.

更新日期/Last Update: 2017-09-21