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PNAS: 如何刻画网络的结构一致性及链路可预测性?
热度 3 babyann519 2015-3-28 22:09
Significance Quantifying a network's link predictability allows us to (i) evaluate predictive algorithms associated with the network, (ii) estimate the extent to which the organization of the network is explicable, and (iii) monitor sudden mechanistic changes during the network's evolution. The hypothesis of this paper is that a group of links is predictable if removing them has only a small effect on the network's structural features. We introduce a quantitative index for measuring link predictability and an algorithm that outperforms state-of-the-art link prediction methods in both accuracy and universality. This study provides fundamental insights into important scientific problems and will aid in the development of information filtering technologies. Abstract The organization of real networks usuallyembodies both regularities and irregularities, and, in principle, the formercan be modeled. The extent to which the formation of a network can be explainedcoincides with our ability to predict missing links. To understand networkorganization, we should be able to estimate link predictability. We assume thatthe regularity of a network is reflected in the consistency of structuralfeatures before and after a random removal of a small set of links. Based on theperturbation of the adjacency matrix, we propose a universal structuralconsistency index that is free of prior knowledge of network organization.Extensive experiments on disparate real-world networks demonstrate that (i)structural consistency is a good estimation of link predictability and (ii) aderivative algorithm outperforms state-of-the-art link prediction methods inboth accuracy and robustness. This analysis has further applications inevaluating link prediction algorithms and monitoring sudden changes in evolvingnetwork mechanisms. It will provide unique fundamental insights into theabove-mentioned academic research fields, and will foster the development ofadvanced information filtering technologies of interest to informationtechnology practitioners. 全文下载地址(免费) http://www.pnas.org/content/112/8/2325.full?sid=61535af6-9110-483a-9370-a9389abd7977 更多详情参见周涛老师博客~ http://blog.sciencenet.cn/home.php?mod=spaceuid=3075do=blogid=867520
个人分类: 科研工作|15537 次阅读|3 个评论
Physica A: 2014's Top-5 most downloaded articles 免费下载~
热度 2 babyann519 2015-3-18 22:35
Physica A: Statistical Mechanics and its Applications' s top 5 downloaded articles: Link prediction in complex networks: A survey Evolution of the social network of scientific collaborations Identifying influential nodes in complex networks Discovering the influential users oriented to viral marketing based on online social networks Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events
个人分类: 科研工作|14109 次阅读|2 个评论
Physica A Top 25 Hottest Articles
热度 1 babyann519 2014-8-18 15:50
Physics and Astronomy Physica A: Statistical Mechanics and its Applications January to December 2013 full year RSS Blog This! Print Show condensed 1. Link prediction in complex networks: A survey • Review article Physica A: Statistical Mechanics and its Applications, Volume 390, Issue 6, March 2011, Pages 1150-1170 Lü, Linyuan; Zhou, Tao Cited by Scopus (133) 2. The Peter principle revisited: A computational study Physica A: Statistical Mechanics and its Applications, Volume 389, Issue 3, February 2010, Pages 467-472 Pluchino, Alessandro; Rapisarda, Andrea; Garofalo, Cesare Cited by Scopus (16) 3. Evolution of the social network of scientific collaborations Physica A: Statistical Mechanics and its Applications, Volume 311, Issue 3-4, August 2002, Pages 590-614 Barabási, A.L; Jeong, H; Néda, Z; Ravasz, E; Schubert, A; Vicsek, T Cited by Scopus (814) 4. Social network based microblog user behavior analysis Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 7, April 2013, Pages 1712-1723 Yan, Qiang; Wu, Lianren; Zheng, Lan Cited by Scopus (7) 5. Social structure of Facebook networks Physica A: Statistical Mechanics and its Applications, Volume 391, Issue 16, August 2012, Pages 4165-4180 Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A. Cited by Scopus (21) 6. A mathematical model of Gone with the Wind Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 15, August 2013, Pages 3231-3239 Rinaldi, Sergio; Della Rossa, Fabio; Landi, Pietro Cited by Scopus (2) 7. Identifying influential nodes in complex networks Physica A: Statistical Mechanics and its Applications, Volume 391, Issue 4, February 2012, Pages 1777-1787 Chen, Duanbing; Lü, Linyuan; Shang, Ming-Sheng; Zhang, Yi-Cheng; Zhou, Tao Cited by Scopus (42) 8. Mean-field theory for scale-free random networks Physica A: Statistical Mechanics and its Applications, Volume 272, Issue 1-2, October 1999, Pages 173-187 Barabási, Albert-László; Albert, Réka; Jeong, Hawoong 9. Discovering the influential users oriented to viral marketing based on online social networks Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 16, August 2013, Pages 3459-3469 Zhu, Zhiguo 10. The Power Grid as a complex network: A survey Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 11, Pages 2688-2700 Pagani, Giuliano Andrea; Aiello, Marco Cited by Scopus (19) 11. Theory of rumour spreading in complex social networks Physica A: Statistical Mechanics and its Applications, Volume 374, Issue 1, January 2007, Pages 457-470 Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M. Cited by Scopus (105) 12. Identification of overlapping community structure in complex networks using fuzzy c-means clustering Physica A: Statistical Mechanics and its Applications, Volume 374, Issue 1, January 2007, Pages 483-490 Zhang, Shihua; Wang, Rui-Sheng; Zhang, Xiang-Sun Cited by Scopus (176) 13. Multifractal detrended fluctuation analysis of nonstationary time series Physica A: Statistical Mechanics and its Applications, Volume 316, Issue 1-4, December 2002, Pages 87-114 Kantelhardt, Jan W.; Zschiegner, Stephan A.; Koscielny-Bunde, Eva; Havlin, Shlomo; Bunde, Armin; Stanley, H.Eugene Cited by Scopus (790) 14. A study of the spreading scheme for viral marketing based on a complex network model Physica A: Statistical Mechanics and its Applications, Volume 389, Issue 4, February 2010, Pages 859-870 Yang, Jianmei; Yao, Canzhong; Ma, Weicheng; Chen, Guanrong Cited by Scopus (9) 15. Detect overlapping and hierarchical community structure in networks Physica A: Statistical Mechanics and its Applications, Volume 388, Issue 8, April 2009, Pages 1706-1712 Shen, Huawei; Cheng, Xueqi; Cai, Kai; Hu, Mao-Bin Cited by Scopus (131) 16. Simulation of pedestrian dynamics using a two-dimensional cellular automaton Physica A: Statistical Mechanics and its Applications, Volume 295, Issue 3-4, June 2001, Pages 507-525 Burstedde, C; Klauck, K; Schadschneider, A; Zittartz, J Cited by Scopus (528) 17. Correlation of financial markets in times of crisis Physica A: Statistical Mechanics and its Applications, Volume 391, Issue 1-2, January 2012, Pages 187-208 Sandoval, Leonidas; Franca, Italo De Paula Cited by Scopus (27) 18. Scale-free characteristics of random networks: the topology of the world-wide web Physica A: Statistical Mechanics and its Applications, Volume 281, Issue 1-4, June 2000, Pages 69-77 Barabási, Albert-László; Albert, Réka; Jeong, Hawoong 19. The network analysis of urban streets: A dual approach Physica A: Statistical Mechanics and its Applications, Volume 369, Issue 2, September 2006, Pages 853-866 Porta, Sergio; Crucitti, Paolo; Latora, Vito Cited by Scopus (128) 20. Continuous growth models in terms of generalized logarithm and exponential functions • Review article Physica A: Statistical Mechanics and its Applications, Volume 387, Issue 23, October 2008, Pages 5679-5687 Martinez, Alexandre Souto; González, Rodrigo Silva; Terçariol, César Augusto Sangaletti Cited by Scopus (14) 21. Models of vehicular traffic: An engineering perspective Physica A: Statistical Mechanics and its Applications, Volume 372, Issue 1, December 2006, Pages 151-161 Chakroborty, Partha Cited by Scopus (13) 22. Identifying influential nodes in weighted networks based on evidence theory Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 10, Pages 2564-2575 Wei, Daijun; Deng, Xinyang; Zhang, Xiaoge; Deng, Yong; Mahadevan, Sankaran Cited by Scopus (27) 23. Vulnerability analysis of interdependent infrastructure systems: A methodological framework Physica A: Statistical Mechanics and its Applications, Volume 391, Issue 11, June 2012, Pages 3323-3335 Wang, Shuliang; Hong, Liu; Chen, Xueguang Cited by Scopus (13) 24. Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory: A brief review • Review article Physica A: Statistical Mechanics and its Applications, Volume 392, Issue 21, November 2013, Pages 5261-5282 Kerner, Boris S. Cited by Scopus (9) 25. Sand dunes mobility and stability in relation to climate Physica A: Statistical Mechanics and its Applications, Volume 357, Issue 1, November 2005, Pages 50-56 Tsoar, H. Cited by Scopus (93)
个人分类: 科研工作|13265 次阅读|1 个评论
《链路预测》
热度 14 babyann519 2013-9-21 15:31
经过两年多的努力《链路预测》这本小书终于和大家见面了!在这两年多的准备过程中,可谓痛并快乐着,第一次的出版经历让我兴奋激动的同时也常常纠结忧愁,相比之下,拥有多次出书经历的本书的合作者周涛老师就显得从容淡定许多,在本书的撰写过程中他对于我的帮助是巨大的,这些都将成为我一生的财富! 要感谢的人太多太多,特别是我的家人,没有他们的悉心照料我是无法有足够的信心和精力完成本书的。这里特别要感谢一位女士Mrs. Yang。你给我的勇气的鼓励成为我前进的无限动力,你和蔼的面容和阳光般的微笑让我充满力量。在这里我要和你一起分享我此刻的喜悦,希望我的快乐也能够使你充满力量,永远的健康快乐! 前言 预测是一切可称之为科学的学科所不能回避的问题。一切不能转化为某种预测的理论都是不值得信赖的 , 与此同时 , 一切坐在神坛上不可一世的理论都时时刻刻战战兢兢地接受着预测的挑战 , 一旦它的预测被证明是不正确的 , 固若金汤的神坛就轰然崩塌了。 亲爱的读者 , 你现在看到的 , 是一本专门讲预测的书。和大家以前经常遇到的股价预测、水文预测等不同 , 本书不关注从一个时间序列的历史中预测未来 ; 与量子力学中对微观粒子状态和运动的预测也不同 , 本书并不依赖于某种第一性原理。本书所关心的问题 , 是在一个网络中 , 如何通过已经观察到的节点之间的连接 , 来重现因为数据缺失尚未观察到的连接 , 或者预测未来将要出现的连接。 网络已经成为描述形形色色复杂系统最重要的工具之一。来自物理学、生物学、信息学、经济学、管理学等越来越多学科的学者 , 都已经认识到 , 真实系统的复杂行为 , 包括演化方向、标度涌现、脆弱性和鲁棒性、群集协同行为等 , 都不仅根植于个体的行为 , 还源于个体与个体之间的相互作用。网络中的链路预测问题 , 得益于学术界对网络科学本身重要性的认识 , 也成为横跨多个学科的核心科学问题。链路预测算法 , 可以帮助提高生物实验的效率 , 可以用于微博中的关注对象推荐和电子商务中的个性化产品推荐 , 甚至可以用来预测美国联邦最高法院法官的投票。链路预测是一大类普适问题的抽象 , 在未来的科学和工程中将扮演越来越重要的角色。 我们和很多同行与高等教育出版社共同力推“网络科学与工程丛书” , 正是因为看到了网络将在未来的多学科交叉中起到中枢的作用。网络科学自身的发展 , 就像其他一切成熟和正在成熟的科学一样 , 需要经历预测和控制的检验。一切的演化模型和动力学分析 , 最终都需要视其能否给出更精确的预测和更高效的控制来判断价值。尽管本书不奢求也不可能解决有关网络预测中的所有问题 , 但是我们相信 , 它必将对“网络科学与工程” 发展成为一个成熟学科贡献自己的匹砖片瓦。 本书共分为九章 , 第一章介绍了关于网络的基本概念 , 可以将本套丛书中另外两本—— 《网络度分布理论》和《网络科学导论》作为参考阅读。第一章并不是《网络科学导论》的一个子集 , 相反 , 我们从网络分类和网络刻画方面给出了更宏观更全面的叙述。从第二章开始我们就进入了链路预测的世界。我们首先给出链路预测的基本概念 , 包括问题的背景和意义 , 问题的数学描述 , 数据集划分的方法和预测精度的评价方法。第三章 , 我们介绍了链路预测中最简洁的框架———基于相似性的链路预测 , 给出目前的 20 余种相似性指标的定义。最后给出了这些指标在 8 个真实网络中的预测精度比较结果 , 据我们所知 , 这应该是目前最全面的一次比较。第四章 , 我们将介绍关于链路预测最复杂的框架———基于似然分析的链路预测模型。这一章体现了链路预测的繁复之美 , 如同一件艺术品值得细细品味。第五章至第七章 , 我们将分别针对含权网络、有向网络和二部分网络进行更加有针对性的方法介绍。对于关注特定类型网络的读者来说 , 你一定能在这些章节中找到适合自己的武器。链路预测在理论上的研究已经是极富挑战而又充满乐趣的事情了 , 但是相比较其应用价值的开发还只是冰山一角 , 且略显平凡。幸运的是 , 我们意识到这一点并在这个方向上持续努力 , 已经取得一些成果。在第八章中 , 我们将结合一些实际的应用场景来进一步展现链路预测的意义和价值所在。希望这一章的内容可以起到抛砖引玉的作用 , 激发更多有价值的应用研究出世 , 并产生越来越多真正的社会经济贡献 , 这才是这个研究方向生机勃发的基本动力。第九章给出了一个小结 , 我们想说的是 , 这虽然是本书的结束 , 但是在链路预测研究的道路上我们一直在前行———任重而道远。 对于大部分读者来说这可能并不是一本有趣的读物 , 但是我们相信它绝对称得上是一本有用的小书。如果书中能有一句话、一幅图 , 或者仅仅是一个短语、一个公式能够让大家有所启发或带来灵感 , 这就是我们最大的成功。这本书有很多出众的地方 , 比如说读者从这本书中看到的绝不仅仅是链路预测的理念和方法 , 还包括了对于各种类型复杂网络结构和功能的全方位的认识———我们特别注意兼顾了内容的“深” 和“广”。但是 , 这本书仍有很多不足之处 , 比如说链路预测中很重要的一部分内容 , 是机器学习的方法 , 我们都没有讲———不是不重要 , 而是以我们的背景和能力 , 不足以把这一部分写好。与其拼凑文献写两章“看起来很美” 的文字 , 不如留白。 很多学生 , 特别是刚刚入门或者说正准备迈入科学研究行列的青年学者经常发来邮件询问一些基本的概念。我想这本书正是你们所期待的。书中 , 我们详细地给出有关链路预测这个研究方向相关概念的定义和描述 , 同时为了帮助理解还给出很多示例。此外 , 在本书的附录 C 中 , 我们将一些经典算法的程序也整理出来 , 以方便读者阅读、理解和使用 , 我想这应该是本书的一大特色。这些程序代码也将在链路预测小组的网站上公布 (www. linkprediction.org), 可供大家直接下载使用。 我们要感谢为本书做出巨大贡献的几位同学 , 他们是张千明、朱郁筱、王文强和潘黎明。千明在社交网络分析和应用方面很有研究 , 他的一些建议和帮助给予本书增色不少。郁筱系统地整理了本书附录 C 的 Matlab 程序代码 , 并细心地添加了对重要语句或较难语句的解释。文强对于本书第八章应用部分和附录的梳理都有相当贡献。黎明帮助整理了关于书中极大似然模型的部分 , 目前他主要从事链路预测方面的研究工作 , 已取得一些成果 , 例如本书第 四章中介绍的闭路模型就是他的成果。此外 , 黎明还非常认真地帮助我们整理和规范了全书 600 条参考文献的格式 , 这真的不是一件容易的事情 , 需要拿出绣花之耐心 , 对于他的一丝不苟再次表示感谢 ! 除此之外 , 来自汪小帆、陈关荣、史定华教授的建议使得本书在行文上更加严谨。还有一些朋友给予的意见使得我们的工作得以不断地完善和提高 , 这里无法一一列出他们的名字。与他们的讨论与交流让我们颇受启发 , 受益良多。 作者还要感谢家人和朋友在精神上的支持和生活上的照顾 , 以及对作者持续忙碌的理解 ! 他们永远是我们坚强的后盾 ! 特别感谢高等教育出版社刘英女士对本书的持续关注和大力支持。她专业的指导和帮助让我们在撰写的过程中更加得心应手 , 使得本书最终能以最好的形态呈现在大家面前。 最后 , 作者特别感谢每一位读者 , 是你们的阅读 , 使得我们两年以来精心的准备和辛苦的撰写变得有价值 ! 我们相信 , 你们也是未来和我们一道努力将“网络科学与工程” 从一个前沿热点方向建设成为理论体系完善、应用场景丰富的一门成熟学科的战友。 希望有更多的有志之士加入我们的队伍 , 在这条路上 , 一直走下去。 吕琳媛 周涛
个人分类: 科研工作|14839 次阅读|25 个评论
2013年度PhysicaA最火爆论文Top-5
热度 3 babyann519 2013-8-23 11:53
Online Version
个人分类: 科研工作|3966 次阅读|3 个评论
Potential Theory for Directed Networks
babyann519 2013-2-25 14:05
Abstract Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. Inparticular, the microscopic organizing principles of directed networks are less understood than those of undirectednetworks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds toa decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining thepotential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positivesupports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the mostaccurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our maincontribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design thecorresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation. Citation: Zhang Q-M, Lu L, Wang W-Q, Zhu Y-X, Zhou T. (2013) Potential Theory for Directed Networks. PLoS ONE 8(2): e55437. doi:10.1371/journal.pone.0055437 Download: journal.pone.0055437.pdf 相关博文: http://blog.sciencenet.cn/home.php?mod=spaceuid=3075do=blogid=664452
个人分类: 科研工作|4839 次阅读|0 个评论
链路预测实验数据
热度 8 babyann519 2012-12-15 15:59
我们整理了一些用于链路预测实验的数据,大家可以在我们的网站上自由下载。 http://www.linkprediction.org/index.php/link/resource/data 欢迎对网站建设提出各种意见:)
个人分类: 科研工作|6500 次阅读|8 个评论
Elsevier非线性统计物理2011年热门文章Top-20排行榜
热度 6 babyann519 2012-6-2 12:04
Nonlinear and Statistical Physics Top 20 most downloaded articles in this subject, 2011 The articles are listed below hierarchically and based on download data for 2011 from Elsevier's online platform SciVerse ScienceDirect. 1 Applications of ultrasound in food technology: Processing, preservation and extraction Ultrasonics Sonochemistry Chemat, F.; Zill-e-Huma; Khan, M.K. 2 Low-carbon building assessment and multi-scale input-output analysis Communications in Nonlinear Science and Numerical Simulation Chen, G.Q.; Chen, H.; Chen, Z.M.; Zhang, B.; Shao, L.; Guo, S.; Zhou, S.Y.; Jiang, M.M. 3 Using sonochemistry for the fabrication of nanomaterials Ultrasonics Sonochemistry Gedanken, A. 4 Nonlinear dynamics for broadband energy harvesting: Investigation of a bistable piezoelectric inertial generator Physica D: Nonlinear Phenomena Stanton, S.C.; McGehee, C.C.; Mann, B.P. 5 Zinc oxide nano-particles - Sonochemical synthesis, characterization and application for photo-remediation of heavy metal Ultrasonics Sonochemistry Banerjee, P.; Chakrabarti, S.; Maitra, S.; Dutta, B.K. 6 Therapeutic ultrasound an overview Ultrasonics Sonochemistry Mason, T.J. 7 A study of the spreading scheme for viral marketing based on a complex network model Physica A: Statistical Mechanics and its Applications Yang, J.; Yao, C.; Ma, W.; Chen, G. 8 Link prediction in complex networks: A survey Physica A: Statistical Mechanics and its Applications Lu, L.; Zhou, T. 9 The characterization of acoustic cavitation bubbles - An overview Ultrasonics Sonochemistry Ashokkumar, M. 10 Graphene oxide based Pt-TiO"2 photocatalyst: Ultrasound assisted synthesis, characterization and catalytic efficiency Ultrasonics Sonochemistry Neppolian, B.; Bruno, A.; Bianchi, C.L.; Ashokkumar, M. 11 The behavior of a many-particle electrode in a lithium-ion battery Physica D: Nonlinear Phenomena Dreyer, W.; Guhlke, C.; Huth, R. 12 Evacuation dynamics with fire spreading based on cellular automaton Physica A: Statistical Mechanics and its Applications Zheng, Y.; Jia, B.; Li, X.G.; Zhu, N. 13 Mean-field theory for scale-free random networks Physica A: Statistical Mechanics and its Applications Barabasi, A.-L.; Albert, R.; Jeong, H. 14 Improved extraction of vegetable oils under high-intensity ultrasound and/or microwaves Ultrasonics Sonochemistry Cravotto, G.; Boffa, L.; Mantegna, S.; Perego, P.; Avogadro, M.; Cintas, P. 15 Sonochemical synthesis of TiO"2 nanoparticles on graphene for use as photocatalyst Ultrasonics Sonochemistry Guo, J.; Zhu, S.; Chen, Z.; Li, Y.; Yu, Z.; Liu, Q.; Li, J.; Feng, C.; Zhang, D. 16 The Peter principle revisited: A computational study Physica A: Statistical Mechanics and its Applications Pluchino, A.; Rapisarda, A.; Garofalo, C. 17 Evolution of the social network of scientific collaborations Physica A: Statistical Mechanics and its Applications Barabasi, A.L.; Jeong, H.; Neda, Z.; Ravasz, E.; Schubert, A.; Vicsek, T. 18 Ultrasonic pretreatment of sludge: A review Ultrasonics Sonochemistry Pilli, S.; Bhunia, P.; Yan, S.; LeBlanc, R.J.; Tyagi, R.D.; Surampalli, R.Y. 19 Advanced oxidation processes (AOPs) involving ultrasound for waste water treatment: A review with emphasis on cost estimation Ultrasonics Sonochemistry Mahamuni, N.N.; Adewuyi, Y.G. 20 Fast and easy synthesis of core-shell nanocrystal (CdS/TiO"2) at low temperature by micro-emulsion under ultrasound Ultrasonics Sonochemistry Ghows, N.; Entezari, M.H.
5827 次阅读|16 个评论
链路预测——网络模型的试金石
babyann519 2012-5-20 22:05
Evaluating network models: A likelihood analysis 作者:Wen-Qiang Wang, Qian-Ming Zhang and Tao Zhou 发表期刊:EPL 98 (2012) 28004. 全文 链 接 http://iopscience.iop.org/0295-5075/98/2/28004 大量生物、技术、信息系统都可以用网络来刻画,其中诸如互联网、万维网、 P2P 网络、在线社交网络等都是信息科学特别关注的对象。网络在演化生长的过程中表现出很多有趣的结构特征,比如集聚性、社团性、无标度性、小世界性等等。建立网络模型重现观察到的结构特性,是最常见的理解网络生长过程潜在驱动力量的方法。针对同一类网络甚至同一个网络,往往有多个理论模型,每一个模型都声称能够捕捉真实网络某几个方面的特征。由于刻画网络结构的特征量成百上千,数不胜数,模型 1 可能在刻画特征 A, B, C 方面胜过其他模型,而模型 2 给出最符合特征 D, E 的结果——事实上,直到现在,我们没有一种本质上有效的方法,来判断不同模型的优劣。 王文强等人的论文第一次试图解决这个困难的问题!链路预测是复杂网络的一种重要分析方法,其目标是通过学习当前观察到的网络结构,计算尚未出现连边的节点对之间存在连边的似然。受到这个方法的启发,王文强等人认为每一个演化模型原则上都对应于一种链路预测的算法,因此,如果把当前网络的真实结构看作基于一段时间之前的网络在模型对应的链路预测算法下预测得到的,我们就可以分析当前网络出现的似然。王文强等人认为,让当前网络似然更大的算法所对应的演化模型更好——这实际上提供了一个评价网络模型优劣的不依赖于任何特定的结构特征或特征组的统一的平台。 王文强等人以真实的互联网数据为基准,比较了有代表性的互联网演化模型的优劣。有趣的是,他们发现通过他们的评价机制得出的模型的“最佳参数”在刻画新增节点的性质的时候,要明显好于通过其他办法得到的“最佳参数”。该工作还隐隐约约包含了一个重要的思想,就是以前的研究,总是关注网络从无到有的过程,希望找到一个普适的模型,一股脑刻画网络一生的行为。实际上,网络初始的增长和中后期的增长机制可能是完全不一样的!链路预测更像是关注网络从少到多,从当前到未来的短期行为,这可能更加重要!即便是在一个模型下,通过与真实网络对比,不同增长时期的“恰当的参数值”也会不一样。这个问题在以前的研究中罕有讨论,但是可以通过本文提出的办法进行分析、挖掘。 参考: 
http://blog.sciencenet.cn/home.php?mod=spaceuid=3075do=blogid=572849
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Physica A 2011年最热门文章Top-25
热度 5 babyann519 2012-3-28 18:33
《链路预测综述》成为Physica A 2011年发表文章中的最热门文章~ Physica A: Statistical Mechanics and its Applications January to December 2011 full year 1. !-- /a-- The Peter principle revisited: A computational study Physica A: Statistical Mechanics and its Applications, Volume 389, Issue 3, February 2010, Pages 467-472 Pluchino, A.; Rapisarda, A.; Garofalo, C. !-- Bookmark article/a -- Cited by SciVerse Scopus (5) 2. !-- /a-- Evolution of the social network of scientific collaborations Physica A: Statistical Mechanics and its Applications, Volume 311, Issue 3-4, August 2002, Pages 590-614 Barabasi, A.L.; Jeong, H.; Neda, Z.; Ravasz, E.; Schubert, A.; Vicsek, T. !-- Bookmark article/a -- Cited by SciVerse Scopus (528) 3. !-- /a-- A study of the spreading scheme for viral marketing based on a complex network model Physica A: Statistical Mechanics and its Applications, Volume 389, Issue 4, February 2010, Pages 859-870 Yang, J.; Yao, C.; Ma, W.; Chen, G. !-- Bookmark article/a -- Cited by SciVerse Scopus (2) 4. !-- /a-- Link prediction in complex networks: A survey • Review article Physica A: Statistical Mechanics and its Applications, Volume 390, Issue 6, March 2011, Pages 1150-1170 Lu, L.; 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无标度网络中的链路预测问题研究
热度 3 babyann519 2012-2-18 15:35
无标度网络中的链路预测问题研究 作者: 王 林,商 超 (西安理工大学自动化与信息工程学院,西安 710048) 摘 要: 研究无标度网络中的链路预测问题。针对人造网络和实际社会网络,分别介绍静态和动态2 种链路预测的实现过程,探究利用相似性进行链路预测的可行性,并验证多种相似度计算方法的准确性。对预测结果进行有效性分析,同时根据不同网络特性给出相应的预测算法。 关键词: 复杂网络;信息检索;无标度;链路预测;拓扑结构;相似性 DOI: 10.3969/j.issn.1000-3428.2012.03.023 全文下载: Research on Link Prediction Problem in Scale-free Network.pdf
个人分类: 科研工作|5360 次阅读|7 个评论

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