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题目: Overlapping community detection in complex networks using symmetric binary matrix factorization 期刊: Physical Review E 卷期号: 87 (6) 页码: 062803 作者: ZhongYuan Zhang, Yong Wang, YongYeol Ahn |
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baiyuefei
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风雪
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【答案】应助回帖
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oven1986: 金币+1, 感谢应助。 2013-09-09 18:39:47
oven1986: 检索EPI+1, 感谢应助！ 2013-09-10 17:43:30
oven1986: 金币+1, 感谢应助。 2013-09-09 18:39:47
oven1986: 检索EPI+1, 感谢应助！ 2013-09-10 17:43:30
2楼2013-09-09 13:29:22
baiyuefei
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风雪
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3楼2013-09-09 13:29:36
baiyuefei
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风雪
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★ ★ ★ ★ ★ ★ ★
oven1986: 金币+1, 感谢应助。 2013-09-09 18:40:10
shamolvzhou: 金币+6 2013-09-10 07:38:52
oven1986: 金币+1, 感谢应助。 2013-09-09 18:40:10
shamolvzhou: 金币+6 2013-09-10 07:38:52
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Overlapping community detection in complex networks using symmetric binary matrix factorization 作者: Zhang, ZY (Zhang, Zhong-Yuan)[ 1 ] ; Wang, Y (Wang, Yong)[ 2 ] ; Ahn, YY (Ahn, Yong-Yeol)[ 3 ] 来源出版物: PHYSICAL REVIEW E 卷:87 期:6 文献号:062803 DOI:10.1103/PhysRevE.87.062803 出版年:JUN 12 2013 被引频次: 0 (来自 Web of Science) 引用的参考文献: 33 [ 查看 Related Records ] 引证关系图 摘要: Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real-world networks, demonstrating the effectiveness of our approach. 入藏号:WOS:000320280900003 文献类型: Article 语种: English KeyWords Plus: MODULARITY; PATTERN 通讯作者地址: Zhang, ZY (通讯作者) Cent Univ Finance & Econ, Sch Stat, Beijing 100081, Peoples R China. 地址: [ 1 ] Cent Univ Finance & Econ, Sch Stat, Beijing 100081, Peoples R China [ 2 ] Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China [ 3 ] Indiana Univ, Sch Informat & Comp, Bloomington, IN 47408 USA 电子邮件地址: zhyuanzh@gmail.com; yyahn@indiana.edu 基金资助致谢: 基金资助机构 授权号 National Natural Science Foundation of China 61203295 11131009 61171007 Program for Innovation Research in Central University of Finance and Economics [显示基金资助信息] 出版商:AMER PHYSICAL SOC, ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA Web of Science 类别: Physics, Fluids & Plasmas; Physics, Mathematical 研究方向: Physics IDS 号:162QS ISSN:1539-3755 |
4楼2013-09-09 13:30:10
baiyuefei
版主 (文学泰斗)
风雪
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【答案】应助回帖
★ ★ ★ ★ ★
oven1986: 金币+1, 感谢应助。 2013-09-09 18:40:21
shamolvzhou: 金币+4 2013-09-10 07:38:35
oven1986: 金币+1, 感谢应助。 2013-09-09 18:40:21
shamolvzhou: 金币+4 2013-09-10 07:38:35
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EI也可以检索到，两个结果，你自己挑那个是你需要的吧： Accession number: 13551901 Title: Overlapping community detection in complex networks using symmetric binary matrix factorization Authors: Zhong-Yuan Zhang1 ; Yong Wang2; Yong-Yeol Ahn3 Author affiliation: 1Sch. of Stat., Central Univ. of Finance & Econ., Beijing, China 2Nat. Center for Math. & Interdiscipl. Sci., Acad. of Math. & Syst. Sci., Beijing, China 3Sch. of Inf. & Comput., Indiana Univ., Bloomington, IN, United States Source title: Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) Abbreviated source title: Phys. Rev. E, Stat. Nonlinear Soft Matter Phys. (USA) Volume: 87 Issue: 6 Publication date: June 2013 Pages: 062803 (7 pp.) Language: English ISSN: 1539-3755 CODEN: PLEEE8 Document type: Journal article (JA) Publisher: American Physical Society Country of publication: USA Material Identity Number: DQ95-2013-006 Abstract: Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real-world networks, demonstrating the effectiveness of our approach. Number of references: 35 Inspec controlled terms: complex networks - matrix decomposition - network theory (graphs) - social sciences Uncontrolled terms: community detection - community structure quality - partition density - community membership - overlapping community identification - symmetric binary matrix factorization - complex network Inspec classification codes: C1290P Systems theory applications in social science and politics - C1110 Algebra - C1160 Combinatorial mathematics Treatment: Theoretical or Mathematical (THR) Discipline: Computers/Control engineering (C) DOI: 10.1103/PhysRevE.87.062803 Database: Inspec Copyright 2013, The Institution of Engineering and Technology Full-text and Local Holdings Links 另一个： Accession number: 20132716470678 Title: Overlapping community detection in complex networks using symmetric binary matrix factorization Authors: Zhang, Zhong-Yuan1 ; Wang, Yong2; Ahn, Yong-Yeol3 Author affiliation: 1School of Statistics, Central University of Finance and Economics, Haidian District, Beijing 100081, China 2National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China 3School of Informatics and Computing, Indiana University, Bloomington, IN 47408, United States Corresponding author: Zhang, Z.-Y. (zhyuanzh@gmail.com) Source title: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics Abbreviated source title: Phys. Rev. E Stat. Nonlinear Soft Matter Phys. Volume: 87 Issue: 6 Issue date: June 12, 2013 Publication year: 2013 Article number: 062803 Language: English ISSN: 15393755 E-ISSN: 15502376 CODEN: PLEEE8 Document type: Journal article (JA) Publisher: American Physical Society, One Physics Ellipse, College Park, MD 20740-3844, United States Abstract: Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real-world networks, demonstrating the effectiveness of our approach. © 2013 American Physical Society. Number of references: 35 Main heading: Quality control Controlled terms: Social sciences Uncontrolled terms: Binary matrix - Community structures - Overlapping communities - Overlapping community detections - Overlapping nodes - Real-world networks - Structure and dynamics - Synthetic benchmark Classification code: 913.3 Quality Assurance and Control - 971 Social Sciences DOI: 10.1103/PhysRevE.87.062803 Database: Compendex Compilation and indexing terms, © 2013 Elsevier Inc. |
5楼2013-09-09 13:32:26
