收稿日期: 2016-10-21
修回日期: 2016-12-10
网络出版日期: 2017-09-30
基金资助
国家自然科学基金（No.61163010）；兰州市科技计划项目基金（No.2014-1-171）；金川公司预研基金（No.JCYY2013012）资助
Community Tracking Algorithm Based on Active Points
Received date: 2016-10-21
Revised date: 2016-12-10
Online published: 2017-09-30
杨绍文, 闫光辉, 李雷, 张海涛 . 基于活跃点的社区跟踪算法[J]. 应用科学学报, 2017 , 35(5) : 602 -611 . DOI: 10.3969/j.issn.0255-8297.2017.05.006
The research of complex networks is mainly aimed at the complex systems, and it is a general method for dealing with various problems in complex systems. In general, complex network community tracking neglects evolutionary time domain factors and differences in the evolution of network members. This paper proposes a community tracking method that includes time domain information in the similarity function, and extracts active nodes in the network by taking into account smoothness of network evolution and differences between nodes. Experiments show that the proposed algorithm fnds the community evolution process better than those based on DBLP data sets. It can also discover community similarity effectively.
[1] Watts D J, Strogatz S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684):440-442.
[2] Erdos P, Renyi A. On the evolution of random graphs[J]. Publication of the Mathematical Institute of the Hungarian Academy Ofences, 2012, 38(1):17-61.
[3] Barabasi A L, Albert R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439):509-512.
[4] Girvan M, Newman M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12):7821-7826.
[5] Fortunato S. Community detection in graphs[J]. Physics Reports, 2010, 486:75-174.
[6] Gergely P, Albert-Laszlo B, Vicsek T. Quantifying social group evolution[J]. Nature, 2007, 446(7136):664-671.
[7] Holme P, Saramaki J. Temporal networks[J]. Physics Reports, 2012, 519(3):97-125.
[8] Kumar R, Novak J, Tomkins A. Structure and evolution of online social networks[J]. Association for Computing Machinery, 2010:337-357.
[9] Yongjin P, Bader J S. How networks change with time[J]. Bioinformatics, 2012, 28(12):40-48.
[10] Aral S, Walker D. Identifying influential and susceptible members of social networks[J]. Science, 2012, 337(6092):337-41.
[11] Valdano E, Ferreri L, Poletto C, Colizza V. Analytical computation of the epidemic threshold on temporal networks[J]. Physical Review X, 2015, 18(3):503-512.
[12] Toyoda M, Kitsuregama M. Extracting evolution of Web communities from a series of Web archives[J]. The Fourteenth ACM Conference on Hypertext and Hypermedia, 2003:28-37.
[13] Hopcroft J, Khan O, Kulis B, Selman B. Tracking evolving communities in large linked networks[J]. Proceedings of the National Academy of Sciences, 2004(1):5249-53.
[14] Asur S, Parthasarathy S, Ucar D. An event-based framework for characterizing the evolutionary behavior of interaction graphs[J]. Acm Transactions on Knowledge Discovery from Data, 2009, 3(4):913-921.
[15] Greene D, Doyle D, Cunningham P. Tracking the evolution of communities in dynamic social networks[C]//International Conference on Advances in Social Networks Analysis and Mining, 2010:176-183.
[16] Mansoureh T, Justin F, Farzad S, Zaiane O R. Tracking changes in dynamic information networks[C]//International Conference on Computational Aspects of Social Networks. 2011:94-101.
[17] Stanislaw S, Bogdan G, Piotr B. Predicting community evolution in social networks[J]. Entropy, 2015, 17(5):3053-3096.
[18] Goldberg M, Magdonismail M, Nambirajan S, Thompson J. Tracking and predicting evolution of social communities[C]//IEEE Third International Conference on Privacy, 2011:780-783.
[19] Gauvin L, Panisson A, Cattuto C. Detecting the community structure and activity patterns of temporal networks:a non-negative tensor factorization approach[J]. Plos One, 2014, 546(2):277-82.
[20] 王翼,吴斌,杨胜琦. CommTracker:一种基于核心的社区演化跟踪算法[J]. 计算机科学与探索,2009, 3(3):282-292. Wang Y, Wu B, Yang S Q. CommTracker:a core-based algorithm of tracking community evolution[J]. Journal of Frontiers of Computer Science and Technology, 2009, 3(3):282-292. (in Chinese)
[21] Ley M. DBLP:some lessons learned[J]. Proceedings of the VLDB endowment, 2009, 2(2):1493-1500.
[22] Subelj L, Bajec M. Robust network community detection using balanced propagation[J]. Physics of Condensed Matter, 2011, 81(3):353-362.
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