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基于信号传递与层次聚类的社团发现算法
  • 期刊名称：计算机工程与应用
  • 时间：2010.3.3
  • 页码：51-54
  • 分类：N94[自然科学总论—系统科学] TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]山东师范大学管理与经济学院,济南250014
  • 相关基金：国家自然科学基金（the National Natural Science Foundation of China under Grant No.60673047 ）;山东省自然科学基金（the Natural Science Foundation of Shandong Province of China）;山东省教育厅科技项目（No.J07YJ02）.
  • 相关项目：社交网络模型构建及虚拟社区性质的研究
作者： 黄浩英|马英红|
关键词： 社团结构, 信号传递, 层次聚类, 模块度, community structure, signal process, hierarchical clustering, modularity
中文摘要：

社团是社会网络的一个重要特征,社团发现是近年来研究的热点问题之一。通过在复杂网络上传递信号,获得各节点对网络的影响向量,从而把网络中节点的拓扑性质转化为代数空间上向量的几何关系,然后用结合模块度的层次聚类挖掘社会网络中的社团结构。该算法优点是不需要预先知道社团的数量或社团内节点的数量,用Zachary空手道俱乐部网络、大学足球赛网络以及海豚关系网络的数据进行验证,该算法划分的社团准确性超过了Newman的结论。

英文摘要：

Community is one of important characters in social networks and community detecting is also a fashionable statement recently.In this paper,based on signaling process on complex networks,influence vectors of each node are got,topological structure of each node is translated into geometrical relationships of vectors in algebra spaces,and by the aid of hierarchical clustering modularity method,communities are detected effectively.With data simulations on the Zachary Karate Club network,College Football network and Dolphin social network,it shows that the proposed algorithm in this topic is more accurate than Newman＇s.

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期刊论文 38
社交网络模型构建及虚拟社区性质的研究
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