...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge-Based Systems >IncOrder: Incremental density-based community detection in dynamic networks
【24h】

IncOrder: Incremental density-based community detection in dynamic networks

机译：IncOrder：动态网络中基于增量的密度社区检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, an incremental density-based clustering algorithm IncOrder is proposed for detecting communities in dynamic networks. It consists of two separate stages: an online stage and an offline stage. The online stage maintains the traversal sequence of a network and the offline stage extracts communities from the sequence. Based on a symmetric measure core-connectivity-similarity between pairs of adjacent nodes, the online stage builds an index structure, called core-connected chain, for dynamic networks. Since the slight change of a network has a very limited impact on its cluster chain, the chain of a dynamic network can be efficiently preserved. The offline stage extracts all possible density-based clustering results for all similarity thresholds from the chain. By maximizing a modularity function, the proposed method can automatically select the parameter of similarity threshold. Experimental results on a large number of real-world and synthetic networks show that the proposed method achieves high accuracy and efficiency.
机译：本文提出了一种基于增量密度的聚类算法IncOrder，用于检测动态网络中的社区。它包含两个单独的阶段：在线阶段和离线阶段。在线阶段维护网络的遍历序列，离线阶段从该序列中提取社区。在线阶段基于对相邻节点对之间的核心连接相似性的对称度量，为动态网络建立了一个称为核心连接链的索引结构。由于网络的微小变化对其集群链的影响非常有限，因此可以有效地保留动态网络的链。离线阶段从链中提取所有相似度阈值的所有可能的基于密度的聚类结果。通过最大化模块化功能，该方法可以自动选择相似度阈值的参数。在大量真实世界和合成网络上的实验结果表明，该方法具有很高的准确性和效率。

著录项

  • 来源
    《Knowledge-Based Systems》 |2014年第12期|1-12|共12页
  • 作者

    Heli Sun; Jianbin Huang; Xin Zhang; Jiao Liu; Dong Wang; Huailiang Liu; Jianhua Zou; Qinbao Song;

  • 作者单位

    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China,State Key Laboratory for Novel Software Technology, Nanjing University. Nanjing 210023, China;

    School of Software, Xidian University, Xi'an 710071, China,State Key Laboratory for Novel Software Technology, Nanjing University. Nanjing 210023, China;

    School of Software, Xidian University, Xi'an 710071, China;

    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    School of Information Technology, Northwest University, Xi'an 710127, China;

    School of Economics and Management, Xidian University, Xi'an 710071, China;

    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

    School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Dynamic network; Community detection; Density-based clustering; Incremental method; Modularity optimazition;

    机译：动态网络;社区检测;基于密度的聚类;增量法;模块化优化;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号