| 基于节点属性的社群结构探测算法改进 |
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| 引用本文： | 张锴琦,杜海峰,蔡萌,费尔德曼.基于节点属性的社群结构探测算法改进[J].系统工程理论与实践,2013,33(11):2879-2886. |
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| 作者姓名： | 张锴琦 杜海峰 蔡萌 费尔德曼 |
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| 作者单位： | 1. 西安交通大学 管理学院, 西安 710049;
2. 西安交通大学 公共管理与复杂性科学研究中心, 西安 710049;
3. 斯坦福大学 人口与资源研究所, CA 94305 |
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| 基金项目： | 国家自然科学基金(71071128);国家社会科学基金重点项目(12AZD110);中央高校基本科研业务费专项资金;霍英东教育基金会(121093) |
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| 摘 要： | 对Vincent D. Blondel等提出的B算法的特点及机理进行了分析, 讨论了节点属性对社群结构探测的可能影响. 进而通过重构初始化网络, 控制节点(社群)合并过程两个方面, 对B算法进行了改进, 获得更优的模块性指标及对应的社群划分. 经计算机模拟网络与实际网络的社群结构探测, 结果表明所提改进算法有效可用, 能在获得较大模块性指标的同时, 获得较好的社群划分结果, 且拥有更低的运算时间.
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| 关 键 词： | 复杂网络 社群结构 节点属性 算法改进 |
| 收稿时间： | 2011-11-14 |
Improved community structure detection algorithm based on the node’s property |
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| ZHANG Kai-qi,DU Hai-feng,CAI Meng,Marcus W. Feldman.Improved community structure detection algorithm based on the node’s property[J].Systems Engineering —Theory & Practice,2013,33(11):2879-2886. |
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| Authors: | ZHANG Kai-qi DU Hai-feng CAI Meng Marcus W Feldman |
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| Institution: | 1. School of Management, Xi'an Jiaotong University, Xi'an 710049, China;
2. Center for Administration and Complexity Science, Xi'an Jiaotong University, Xi'an 710049, China;
3. Morrison Institute for Population and Resource Studies, Stanford University, CA 94305, USA |
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| Abstract: | This paper analyzes the features and mechanism of the B algorithm proposed by Vincent D. Blondel et al., and discusses the possible impact of nodes' property on community structure detection. Then it proposes an improved algorithm for the B algorithm by reconstructing the initial network and controlling node (community) merging process in order to obtain better modularity and the corresponding network partition. The community structure detection experiments on computer simulation networks and actual networks, show that the improved algorithm we proposed is reliable and effective, which achieves a better network partition with a larger modularity and has shorter computation time. |
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| Keywords: | complexity network community structure node property algorithm improvement |
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