Please wait a minute...
Advanced Search
数据分析与知识发现  2017, Vol. 1 Issue (9): 49-56     https://doi.org/10.11925/infotech.2096-3467.2017.09.05
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于矩阵分解学习的科学合作网络社区发现研究*
施晓华1,2(), 卢宏涛2
1上海交通大学图书馆 上海 200240
2上海交通大学计算机系 上海 200240
Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF
Shi Xiaohua1,2(), Lu Hongtao2
1Library of Shanghai Jiaotong University, Shanghai 200240, China
2Computer Science Department, Shanghai Jiaotong University, Shanghai 200240, China
全文: PDF (2845 KB)   HTML ( 2
输出: BibTeX | EndNote (RIS)      
摘要 

目的】在科学合作网络的发展及主要社区发现方法的基础上, 提出发现合作网络社区信息的方法。【方法】以情报领域部分相关期刊2012年-2016年发表论文的共著网络为实验数据, 基于贝叶斯对称非负矩阵分解方法, 结合自动相关确定稀疏压缩原理, 实现社区数量的自动获取, 并在分解过程中应用对称矩阵分解原理。【结果】通过与现有方法的比较与分析, 本文方法得到较好的实验结果。【局限】网络数据获取中未引入学者甄别的优化方法。【结论】本文提出的方法能有效解决合作网络社区发现需求。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
施晓华
卢宏涛
关键词 科学网络文献共著网络社区发现非负矩阵分解贝叶斯方法    
Abstract

[Objective] This study proposes and examines a new method to identify the communities in collaboration network of scientific researchers. [Methods] First, we retrieved the need data from information science journal articles published from 2012 to 2016. Then, we used the Automatic Relevance Determination to find the target community with the Bayesian Symmetric Non-negative Matrix Factorization method. Finally, we compared the performance of our method with the existing ones. [Results] The proposed method got better results than others. [Limitations] Did not optimize our data with the researcher identifications. [Conclusions] The proposed method could effectively find communities from the scientific collaboration network.

Key wordsScientific Network    Co-author Network    Community Detection    Non-negative Matrix Factorization    Bayesian Approach
收稿日期: 2017-04-10      出版日期: 2017-10-18
ZTFLH:  G252  
基金资助:*本文系上海交通大学2013年文理交叉项目“科学网络中知识社区发现技术与应用研究”(项目编号: 13JCY14)的研究成果之一
引用本文:   
施晓华, 卢宏涛. 基于矩阵分解学习的科学合作网络社区发现研究*[J]. 数据分析与知识发现, 2017, 1(9): 49-56.
Shi Xiaohua,Lu Hongtao. Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF. Data Analysis and Knowledge Discovery, 2017, 1(9): 49-56.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.09.05      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I9/49
  BSNMF过程演示
姓名 单位 发表次数 网络度数
邱均平 武汉大学 50 66
朱庆华 南京大学 41 83
黄鲁成 北京工业大学 40 118
赵蓉英 武汉大学 36 49
陈福集 福州大学 35 48
王国华 华中科技大学 30 89
谢阳群 淮北师范大学 27 48
娄策群 华中师范大学 26 44
张玉峰 武汉大学 26 37
孙建军 南京大学 25 42
  发表论文前10的作者信息
方法 模块度
3-Clique 0.3579
GN 0.5530
BGLL 0.8294
Louvain 0.9165
NMF 0.4209
SNMF 0.8165
BSNMF 0.9664
  社区发现结果模块度比较
社区 节点数 节点度和 主要节点人员
(度大于10, 下划线为大于20)
1 103 200 孙建军, 俞立平, 郑彦宁, 潘云涛,
武夷山, 丁堃, 姜春林, 刘志辉
2 100 192 朱庆华, 袁勤俭, 宗乾进, 赵宇翔,
刘璇
3 100 220 黄鲁成, 翟东升, 苗红, 吴菲菲,
张杰, 娄岩
4 89 159 毕强, 彭洁, 滕广青, 黄微
5 84 194 王国华, 曾润喜, 钟声扬, 陈强, 王
雅蕾, 杨腾飞, 徐晓林, 张韦, 闵晨
6 84 177 张海涛, 徐宝祥, 张连峰, 崔金栋,
武慧娟, 王欣, 王丹, 许孝君, 宋拓
  其中发现的几个主要社区及节点信息
  利用BSNMF发现的702个社区的节点数统计
  以孙建军等为主要节点的网格形社区结构(黄色节点为度大于10的节点)
  以孙建军等为主要节点合作论文的主题词云图
  以黄鲁成为主要节点的环形社区结构
(黄色节点为度大于10的节点)
[1] Ding Y.Community Detection: Topological vs. Topical[J]. Journal of Informetrics, 2011, 5(4): 498-514.
doi: 10.1016/j.joi.2011.02.006
[2] Newman M E J, Girvan M. Finding and Evaluating Community Structure in Networks[J]. Physical Review E, 2004, 69: Article No. 026113.
doi: 10.1103/PhysRevE.69.026113 pmid: 14995526
[3] Tang X, Xu T, Feng X.et al. Uncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization[J]. PLoS ONE, 2014, 9(12): Article No. e107884.
doi: 10.1371/journal.pone.0107884 pmid: 4182427
[4] 徐玲, 胡海波, 汪小帆. 一个中国科学家合作网的实证分析[J]. 复杂系统与复杂性科学, 2009, 6(1): 20-28.
doi: 10.3969/j.issn.1672-3813.2009.01.003
[4] (Xu Ling, Hu Haibo, Wang Xiaofan.Empirical Analysis of a China Scientists Collaboration Network[J], Complex System and Complexity Science, 2009, 6(1): 20-28.)
doi: 10.3969/j.issn.1672-3813.2009.01.003
[5] Bailón-Moreno R, Jurado-Alameda E, Ruiz-Baños R.The Scientific Network of Surfactants: Structural Analysis[J]. Journal of the American Society for Information Science and Technology, 2006, 57(7): 949-960.
doi: 10.1002/asi.20362
[6] Quattrociocchi W, Amblard F, Galeota E.Selection in Scientific Networks[J]. Social Network Analysis and Mining, 2012, 2(3): 229-237.
doi: 10.1007/s13278-011-0043-7
[7] Havemann F, Scharnhorst A.Bibliometric Networks[OL]. arXiv PrePrint, arXiv: 1212.5211.
[8] Chen P, Redner S.Community Structure of the Physical Review Citation Network[J]. Journal of Informetrics, 2010, 4(3): 278-290.
doi: 10.1016/j.joi.2010.01.001
[9] 马瑞敏, 倪超群. 作者耦合分析; 一种新学科知识结构发现方法的探索性研究[J]. 中国图书馆学报, 2012, 38(2): 4-11.
doi: 10.3969/j.issn.1001-8867.2012.02.001
[9] (Ma Ruimin, Ni Chaoqun.Author Coupling Analysis: An Exploratory Study on a New Approach to Discover Intellectual Structure of a Discipline[J]. Journal of Library Science in China, 2012, 38(2): 4-11.)
doi: 10.3969/j.issn.1001-8867.2012.02.001
[10] 张斌. 共词网络的结构与演化: 概念与理论进展[J]. 情报杂志, 2014, 33(7): 103-109.
doi: 10.3969/j.issn.1002-1965.2014.07.019
[10] (Zhang Bin.Structure and Evolution of Co-word Network: Concept and Research Review[J]. Journal of Intelligence, 2014, 33(7): 103-109.)
doi: 10.3969/j.issn.1002-1965.2014.07.019
[11] 苗蕊, 刘鲁. 科学家合作网络中的社区发现[J]. 情报学报, 2011, 30(12): 1312-1318.
doi: 10.3772/j.issn.1000-0135.2011.12.011
[11] (Miao Rui, Liu Lu.Community Detection in Scientific Collabration Network[J]. Journal of the China Society for Science and Technical Information, 2011, 30(12): 1312-1318.)
doi: 10.3772/j.issn.1000-0135.2011.12.011
[12] Newman M E J. Coauthorship Networks and Patterns of Scientific Collaboration[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(S1): 5200-5205.
doi: 10.1073/pnas.0307545100 pmid: 14745042
[13] 王福生, 杨洪勇. 作者科研合作网络模型与实证研究[J]. 图书情报工作, 2007, 51(10): 68-71.
[13] (Wang Fusheng, Yang Hongyong.Author Collaboration Network Model and Demonstration Study[J]. Library and Information Service, 2007, 51(10): 68-71.)
[14] Mimno D.Community-based Link Prediction with Text[C]// Proceedings of the 21st Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada. 2007.
[15] Erfanmanesh M, Rohani V A, Abrizah A.Co-authorship Network of Scientometrics Research Collaboration[J]. Malaysian Journal of Library & Information Science, 2012, 17(3): 73-93.
[16] Fortunato S.Community Detection in Graphs[J]. Physics Reports, 2010, 486(3): 75-174.
doi: 10.1016/j.physrep.2009.11.002
[17] Palla G, Derényi I, Farkas I, et al.Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society[J]. Nature, 2005, 435(7043): 814-818.
doi: 10.1038/nature03607 pmid: 15944704
[18] Blondel V D, Guillaume J L, Lambiotte R, et al.Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008(10): P10008.
[19] Newman M E J. Modularity and Community Structure in Networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577-8582.
doi: 10.1073/pnas.0601602103
[20] Le Martelot E, Hankin C.Fast Multi-scale Detection of Relevant Communities in Large-scale Networks[J]. The Computer Journal, 2013. DOI: 10.1093/comjnl/bxt002.
[21] Lee D D, Seung H S.Learning the Parts of Objects by Non- negative Matrix Factorization[J]. Nature, 1999, 401(6755): 788-791.
doi: 10.1038/44565 pmid: 10548103
[22] 李亚芳, 贾彩燕, 于剑. 应用非负矩阵分解模型的社区发现方法综述[J]. 计算机科学与探索, 2016, 10(1): 1-13.
doi: 10.3778/j.issn.1673-9418.1505047
[22] (Li Yafang, Jia Caiyan, Yu Jian.Survey on Community Detection Algorithms Using Nonnegative Matrix Factorization Model[J]. Journal of Frontiers of Computer Science and Technology, 2016, 10(1): 1-13.)
doi: 10.3778/j.issn.1673-9418.1505047
[23] Wang F, Li T, Wang X, et al.Community Discovery Using Nonnegative Matrix Factorization[J]. Data Mining and Knowledge Discovery, 2011, 22(3): 493-521.
doi: 10.1007/s10618-010-0181-y
[24] Zhang Y, Yeung D Y.Overlapping Community Detection via Bounded Nonnegative Matrix Tri-factorization[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2012: 606-614.
[25] Mankad S, Michailidis G.Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization[J]. Physical Review E, 2013, 88(4): 042812.
doi: 10.1103/PhysRevE.88.042812 pmid: 24229230
[26] Yang J, Leskovec J.Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach[C]// Proceedings of the 6th ACM International Conference on Web Search and Data Mining. ACM, 2013: 587-596.
[27] Xie J, Kelley S, Szymanski B K.Overlapping Community Detection in Networks: The State-of-the-Art and Comparative Study[J]. ACM Computing Surveys, 2013, 45(4): 43.
doi: 10.1145/2501654.2501657
[28] Psorakis I, Roberts S, Ebden M, et al.Overlapping Community Detection Using Bayesian Non-negative Matrix Factorization[J]. Physical Review E, 2011, 83(6): 066114.)
doi: 10.1103/PhysRevE.83.066114 pmid: 21797448
[29] Shi X, Lu H.Community Inference with Bayesian Non-negative Matrix Factorization[A]// Web Technologies and Applications[M]. Springer International Publishing, 2016: 208-219.
[30] Mørup M, Hansen L K.Automatic Relevance Determination for Multi-way Models[J]. Journal of Chemometrics, 2009, 23(7-8): 352-363.
doi: 10.1002/cem.1223
[31] RSS-CNKI [EB/OL]. [2017-03-06]. .
[32] Tang J, Fong A C M, Wang B, et al. A Unified Probabilistic Framework for Name Disambiguation in Digital Library[J]. IEEE Transaction on Knowledge and Data Engineering, 2012, 24(6): 975-987.
doi: 10.1109/TKDE.2011.13
[1] 陈东沂,周子程,蒋盛益,王连喜,吴佳林. 面向企业微博的客户细分框架*[J]. 现代图书情报技术, 2016, 32(2): 43-51.
[2] 刘郝霞, 彭商濂. 一种基于邻近节点影响强度标签传播社区发现方法[J]. 现代图书情报技术, 2015, 31(4): 58-64.
[3] 吴小兰, 章成志. 社会化媒体中的社区发现研究综述[J]. 现代图书情报技术, 2013, 29(10): 36-42.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn