A thermal flux-diffusing model for complex networks and its applications in community structure detection

doi:10.1088/1674-1056/22/5/058903

Abstract We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated.

Keywords: complex networks community structure thermal flux-diffusing model

Received: 04 June 2012
Revised: 19 October 2012
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	89.75.Da	(Systems obeying scaling laws)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 60672095), the Fundamental Research Funds for the Central Universities, China (Grant No. KYZ201300), and the Youth Sci-Tech Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

Corresponding Authors: Shen Yi
E-mail: shen_yi1979@njau.edu.cn

Cite this article:

Shen Yi (沈毅) A thermal flux-diffusing model for complex networks and its applications in community structure detection 2013 Chin. Phys. B 22 058903

[1]	Girvan M and Newman M E J 2002 Proc. Natl. Acad. Sci. USA 99 7821
[2]	Palla G, Derényi I, Farkas I and Vicsek T 2005 Nature 435 814
[3]	Redner S 1998 Eur. Phys. J. B 4 131
[4]	Chen D B, Shang M S, Lv Z H and Fu Y 2010 Physica A 389 4177
[5]	Lancichinetti A and Fortunato S 2009 Phys. Rev. E 80 056117
[6]	Wang X H, Jiao L C and Wu J S 2010 Chin. Phys. B 19 020501
[7]	Zhang B D, Tang Y H, Wu J J and Li X 2011 Chin. Phys. B 20 098901
[8]	Fan C X, Wan Y H and Jiang G P 2012 Chin. Phys. B 21 020510
[9]	Newman M E J and Girvan M 2004 Phys. Rev. E 69 026113
[10]	Newman M E J 2006 Phys. Rev. E 74 036104
[11]	Fortunato S 2010 Physics Reports 486 75
[12]	Flake G W, Lawrence S R, Giles C L and Coetzee F M 2002 IEEE Computer 35 66
[13]	Shen H W, Cheng X Q, Cai K and Hu M B 2009 Physica A 388 1706
[14]	Ahn Y Y, Bagrow J P and Lehmann S 2010 Nature 466 761
[15]	Mucha P J, Richardson T, Macon K, Porter M A and Onnela J P 2010 Science 328 876
[16]	Shen H W, Cheng X Q and Fang B X 2010 Phys. Rev. E 82 016114
[17]	Yang B, Cheung W K and Liu J M 2007 IEEE T. Knowl. Data En. 19 1333
[18]	Bagrow J P 2008 J. Stat. Mech. P05001
[19]	Clauset A 2005 Phys. Rev. E 72 026132
[20]	Cengel Y A 2007 Introduction to Thermodynamics and Heat Transfer + EES Software (New York: McGraw Hill Higher Education Press) pp. 253-262
[21]	Duch J and Arenas A 2005 Phys. Rev. E 72 027104
[22]	Guimerá R, Pardo M S and Amaral L A N 2004 Phys. Rev. E 70 025101
[23]	Zou S R, Peng Y J, Liu A F, Xu X L and He D R 2011 Chin. Phys. B 20 018902
[24]	Shen Y 2011 Chin. Phys. B 20 040511
[25]	Danon L, Guilera A D, Duch J and Arenas A 2005 J. Stat. Mech. P09008
[26]	Danon L, Guilera A D, Duch J and Arenas A 2006 J. Stat. Mech. P11010
[27]	Strehl A and Ghosh J 2002 Journal of Machine Learning Research 3 583
[28]	Albert R and Barabási A L 1999 Science 286 509
[29]	Radicchi F, Castellano C, Cecconi F, Loreto V and Parisi D 2004 Proc. Natl. Acad. Sci. USA 101 2658
[30]	Zachary W W 1977 J. Anthropol. Res. 33 452
[31]	Frank K A 1996 Soc. Networks 18 93
[32]	Reichardt J and Bornholdt S 2004 Phys. Rev. Lett. 93 218701

[1]	Analysis of cut vertex in the control of complex networks Jie Zhou(周洁), Cheng Yuan(袁诚), Zu-Yu Qian(钱祖燏), Bing-Hong Wang(汪秉宏), and Sen Nie(聂森). Chin. Phys. B, 2023, 32(2): 028902.
[2]	Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding Pengli Lu(卢鹏丽) and Wei Chen(陈玮). Chin. Phys. B, 2023, 32(1): 018903.
[3]	Characteristics of vapor based on complex networks in China Ai-Xia Feng(冯爱霞), Qi-Guang Wang(王启光), Shi-Xuan Zhang(张世轩), Takeshi Enomoto(榎本刚), Zhi-Qiang Gong(龚志强), Ying-Ying Hu(胡莹莹), and Guo-Lin Feng(封国林). Chin. Phys. B, 2022, 31(4): 049201.
[4]	Robust H_∞ state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks Yahan Deng(邓雅瀚), Zhongkai Mo(莫中凯), and Hongqian Lu(陆宏谦). Chin. Phys. B, 2022, 31(2): 020503.
[5]	Finite-time synchronization of uncertain fractional-order multi-weighted complex networks with external disturbances via adaptive quantized control Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为). Chin. Phys. B, 2022, 31(10): 100504.
[6]	LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks Gui-Qiong Xu(徐桂琼), Lei Meng(孟蕾), Deng-Qin Tu(涂登琴), and Ping-Le Yang(杨平乐). Chin. Phys. B, 2021, 30(8): 088901.
[7]	Complex network perspective on modelling chaotic systems via machine learning Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Chin. Phys. B, 2021, 30(6): 060506.
[8]	Exploring individuals' effective preventive measures against epidemics through reinforcement learning Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞). Chin. Phys. B, 2021, 30(4): 048901.
[9]	Influential nodes identification in complex networks based on global and local information Yuan-Zhi Yang(杨远志), Min Hu(胡敏), Tai-Yu Huang(黄泰愚). Chin. Phys. B, 2020, 29(8): 088903.
[10]	Identifying influential spreaders in complex networks based on entropy weight method and gravity law Xiao-Li Yan(闫小丽), Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni(倪顺江). Chin. Phys. B, 2020, 29(4): 048902.
[11]	Modeling and analysis of the ocean dynamic with Gaussian complex network Xin Sun(孙鑫), Yongbo Yu(于勇波), Yuting Yang(杨玉婷), Junyu Dong(董军宇)†, Christian B\"ohm, and Xueen Chen(陈学恩). Chin. Phys. B, 2020, 29(10): 108901.
[12]	Pyramid scheme model for consumption rebate frauds Yong Shi(石勇), Bo Li(李博), Wen Long(龙文). Chin. Phys. B, 2019, 28(7): 078901.
[13]	Theoretical analyses of stock correlations affected by subprime crisis and total assets: Network properties and corresponding physical mechanisms Shi-Zhao Zhu(朱世钊), Yu-Qing Wang(王玉青), Bing-Hong Wang(汪秉宏). Chin. Phys. B, 2019, 28(10): 108901.
[14]	Coordinated chaos control of urban expressway based on synchronization of complex networks Ming-bao Pang(庞明宝), Yu-man Huang(黄玉满). Chin. Phys. B, 2018, 27(11): 118902.
[15]	Detecting overlapping communities based on vital nodes in complex networks Xingyuan Wang(王兴元), Yu Wang(王宇), Xiaomeng Qin(秦小蒙), Rui Li(李睿), Justine Eustace. Chin. Phys. B, 2018, 27(10): 100504.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Metrics
Related Articles

A thermal flux-diffusing model for complex networks and its applications in community structure detection

Cite this article:

Online attention