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一种基于因子图模型的半监督社区发现方法

黄立威 李彩萍 张海粟 刘玉超 李德毅 刘艳博

黄立威, 李彩萍, 张海粟, 刘玉超, 李德毅, 刘艳博. 一种基于因子图模型的半监督社区发现方法. 自动化学报, 2016, 42(10): 1520-1531. doi: 10.16383/j.aas.2016.c150261
引用本文: 黄立威, 李彩萍, 张海粟, 刘玉超, 李德毅, 刘艳博. 一种基于因子图模型的半监督社区发现方法. 自动化学报, 2016, 42(10): 1520-1531. doi: 10.16383/j.aas.2016.c150261
HUANG Li-Wei, LI Cai-Ping, ZHANG Hai-Su, LIU Yu-Chao, LI De-Yi, LIU Yan-Bo. A Semi-supervised Community Detection Method Based on Factor Graph Model. ACTA AUTOMATICA SINICA, 2016, 42(10): 1520-1531. doi: 10.16383/j.aas.2016.c150261
Citation: HUANG Li-Wei, LI Cai-Ping, ZHANG Hai-Su, LIU Yu-Chao, LI De-Yi, LIU Yan-Bo. A Semi-supervised Community Detection Method Based on Factor Graph Model. ACTA AUTOMATICA SINICA, 2016, 42(10): 1520-1531. doi: 10.16383/j.aas.2016.c150261

一种基于因子图模型的半监督社区发现方法

doi: 10.16383/j.aas.2016.c150261
基金项目: 

国家自然科学基金 61305055

国家重点基础研究发展计划 2014CB340401

国家自然科学基金 61273213

国家重点基础研究发展计划 973 Program

国家自然科学基金 61035004

详细信息
    作者简介:

    李彩萍  北京市遥感信息研究所高级工程师.2015年获得装备学院博士学位.主要研究方向为系统仿真.E-mail:alninglcp@sina.com

    张海粟  中国人民解放军国防信息学院副教授.2012年获得解放军理工大学博士学位.主要研究方向为数据挖掘.E-mail:zhanghaisu@139.com

    刘玉超  中国指挥与控制学会副秘书长.2012年获得清华大学计算机科学与技术系博士学位.主要研究方向为数据挖掘和机器学习.E-mail:yuchaoliu@163.com

    李德毅  中国电子系统工程研究所研究员.中国工程院院士.国际欧亚科学院院士.1983年获得英国爱丁堡HeriotWatt大学博士学位.主要研究方向为人工智能.E-mail:lidy@cae.cn

    刘艳博  北京市遥感信息研究所工程师.2013年获得国防科技大学硕士学位.主要研究方向为遥感信息应用.E-mail:liuyanbonudt@163.com

    通讯作者:

    黄立威  北京市遥感信息研究所工程师.2014年获得解放军理工大学指挥信息系统学院博士学位.主要研究方向为数据挖掘和机器学习.本文通信作者.E-mail:drhuanglw@163.com

A Semi-supervised Community Detection Method Based on Factor Graph Model

Funds: 

National Natural Science Foundation of China 61305055

National Basic Research Program of China 2014CB340401

National Natural Science Foundation of China 61273213

National Basic Research Program of China 973 Program

National Natural Science Foundation of China 61035004

More Information
    Author Bio:

     Senior engineer at Beijing Institute of Remote Sensing. She received her Ph. D. degree from PLA Institute of Equipment in 2015. Her main research interest is system simulation.E-mail:

     Associate professor at the Institute of National Defense Information. He received his Ph. D. degree in computer science from PLA University of Science and Technology in 2012. His main research interest is data mining.E-mail:

     Deputy secretarygeneral at Chinese Institute of Command and Control. He received his Ph. D. degree from the Department of Computer Science and Technology, Tsinghua University in 2012. His research interest covers data mining and machine learning.E-mail:

     Professor at the Institute of Electronic System Engineering. He is currently an academician of Chinese Academy of Engineering, a member of the International Eurosian Academy of Science. He received his Ph. D. degree in computer science from Heriot-Watt University in Edinburgh, UK. His main research interest is artificial intelligence.E-mail:

     Engineer at Beijing Institute of Remote Sensing. She received her master degree in automation from National University of Defense Technology. Her main research interest is remote sensing information application.E-mail:

    Corresponding author: HUANG Li-Wei  Engineer at Beijing Institute of Remote Sensing. He received his Ph. D. degree in computer science from PLA University of Science and Technology in 2014. His research interest covers data mining and machine learning. Corresponding author of this paper.E-mail:drhuanglw@163.com
  • 摘要: 社区发现是社交网络分析中一个重要的研究方向.当前大部分的研究都聚焦在自动社区发现问题,但是在具有数据缺失或噪声的网络中,自动社区发现算法的性能会随着噪声数据的增加而迅速下降.通过在社区发现中融合先验信息,进行半监督的社区发现,有望为解决上述挑战提供一条可行的途径.本文基于因子图模型,通过融入先验信息到一个统一的概率框架中,提出了一种基于因子图模型的半监督社区发现方法,研究具有用户引导情况下的社交网络社区发现问题.在三个真实的社交网络数据(Zachary社会关系网、海豚社会网和DBLP协作网)上进行实验,证明通过融入先验信息可以有效地提高社区发现的精度,且将我们的方法与一种最新的半监督社区发现方法(半监督Spin-Glass模型)进行对比,在三个数据集中F-measure平均提升了6.34%、16.36%和12.13%.
  • 图  1  FG-SSCD模型的图结构

    Fig.  1  Graphical structure of FG-SSCD

    图  2  Zachary网络的真实社团结构

    Fig.  2  Community structure of Zachary0s karate club network

    图  3  海豚关系网的真实社团结构

    Fig.  3  Community structure of dolphin social network

    图  4  FG-SSCD模型发现的社区结构(无噪声)

    Fig.  4  Community structure from FG-SSCD (without noise)

    图  5  FG-SSCD模型发现的社区结构(有噪声)

    Fig.  5  Community structure from FG-SSCD (with noise)

    图  6  不同噪声比例下的社区发现预测精度对比

    Fig.  6  The prediction accuracy with different noise rate

    图  7  不同噪声比例下, 与Spin-Glass模型相比较, FG-SSCD模型F-measure值提高的比例

    Fig.  7  The percent improvement in F-measure of our approach against Spin-Glass model with different noise rate

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出版历程
  • 收稿日期:  2015-05-05
  • 录用日期:  2016-01-19
  • 刊出日期:  2016-10-20

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