作者：Ternence Zhang
转载注明出处：http://blog.csdn.net/zhangtengyuan23
1) Build the co-author network of the Erdos1 authors (you can use the file from the website https://files.oakland.edu/users/grossman/enp/Erdos1.html or the one we include at Erdos1.htm ). You should build a co-author network of the approximately 510 researchers from the file Erdos1, who coauthored a paper with Erd?s, but do not include Erd?s. This will take some skilled data extraction and modeling efforts to obtain the correct set of nodes (the Erd?s coauthors) and their links (connections with one another as coauthors). There are over 18,000 lines of raw data in Erdos1 file, but many of them will not be used since they are links to people outside the Erdos1 network. If necessary, you can limit the size of your network to analyze in order to calibrate your influence measurement algorithm. Once built, analyze the properties of this network. (Again, do not include Erd?s --- he is the most influential and would be connected to all nodes in the network. In this case, it’s co-authorship with him that builds the network, but he is not part of the network or the analysis.)
2) Develop influence measure(s) to determine who in this Erdos1 network has significant influence within the network. Consider who has published important works or connects important researchers within Erdos1. Again, assume Erd?s is not there to play these roles.
3) Another type of influence measure might be to compare the significance of a research paper by analyzing the important works that follow from its publication. Choose some set of foundational papers in the emerging field of network science either from the attached list (NetSciFoundation.pdf) or papers you discover. Use these papers to analyze and develop a model to determine their relative influence. Build the influence (coauthor or citation) networks and calculate appropriate measures for your analysis. Which of the papers in your set do you consider is the most influential in network science and why? Is there a similar way to determine the role or influence measure of an individual network researcher? Consider how you would measure the role, influence, or impact of a specific university, department, or a journal in network science? Discuss methodology to develop such measures and the data that would need to be collected.
4) Implement your algorithm on a completely different set of network influence data --- for instance, influential songwriters, music bands, performers, movie actors, directors, movies, TV shows, columnists, journalists, newspapers, magazines, novelists, novels, bloggers, tweeters, or any data set you care to analyze. You may wish to restrict the network to a specific genre or geographic location or predetermined size.
5) Finally, discuss the science, understanding and utility of modeling influence and impact within networks. Could individuals, organizations, nations, and society use influence methodology to improve relationships, conduct business, and make wise decisions? For instance, at the individual level, describe how you could use your measures and algorithms to choose who to try to co-author with in order to boost your mathematical influence as rapidly as possible. Or how can you use your models and results to help decide on a graduate school or thesis advisor to select for your future academic work?
6) Write a report explaining your modeling methodology, your network-based influence and impact measures, and your progress and results for the previous five tasks. The report must not exceed 20 pages (not including your summary sheet) and should present solid analysis of your network data; strengths, weaknesses, and sensitivity of your methodology; and the power of modeling these phenomena using network science.
*Your submission should consist of a 1 page Summary Sheet and your solution cannot exceed 20 pages for a maximum of 21 pages.
This is a listing of possible papers that could be included in a foundational set of influential publications in network science. Network science is a new, emerging, diverse, interdisciplinary field
so there is no large, concentrated set of journals that are easy to use to find network papers even though several new journals were recently established and new academic programs in network science are beginning to be offered in universities throughout the
world. You can use some of these papers or others of your own choice for your team’s set to analyze and compare for influence or impact in network science for task #3.
Erd?s, P. and Rényi, A., On Random Graphs, Publicationes Mathematicae, 6: 290-297, 1959.
Albert, R. and Barabási, A-L. Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47-97, 2002.
Bonacich, P.F., Power and Centrality: A family of measures, Am J. Sociology. 92: 1170- 1182, 1987.
Barabási, A-L, and Albert, R. Emergence of scaling in random networks. Science, 286: 509-512, 1999.
Borgatti, S. Identifying sets of key players in a network. Computational and Mathematical Organization Theory, 12: 21-34, 2006.
Borgatti, S. and Everett, M. Models of core/periphery structures. Social Networks, 21: 375-395, October 2000
Graham, R. On properties of a well-known graph, or, What is your Ramsey number? Annals of the New York Academy of Sciences, 328:166-172, June 1979.
Kleinberg, J. Navigation in a small world. Nature, 406: 845, 2000.
Newman, M. Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality. Physical Review E, 64:016132, 2001.
Newman, M. The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA, 98: 404-409, January 2001.
Newman, M. The structure and function of complex networks. SIAM Review, 45:167-256, 2003.
Watts, D. and Dodds, P. Networks, influence, and public opinion formation. Journal of Consumer Research, 34: 441-458, 2007.
Watts, D., Dodds, P., and Newman, M. Identity and search in social networks. Science, 296:1302-1305, May 2002.
Watts, D. and Strogatz, S. Collective dynamics of `small-world‘ networks. Nature, 393: 440-442, 1998.
Snijders, T. Statistical models for social networks. Annual Review of Sociology, 37: 131–153, 2011.
Valente, T. Social network thresholds in the diffusion of innovations, Social Networks, 18: 69-89, 1996.
使用网络测量的影响和冲击
学术研究的技术来确定影响之一是构建和引文或合著网络的度量属性。与人合写一手稿通常意味着一个强大的影响力的研究人员之间的联系。最著名的学术合作者是20世纪的数学家保罗鄂尔多斯曾超过500的合作者和超过1400个技术研究论文发表。讽刺的是,或者不是,鄂尔多斯也是影响者在构建网络的新兴交叉学科的基础科学,尤其是，尽管他与AlfredRényi的出版物“随即图标”在1959年。
鄂尔多斯作为合作者的角色非常重要领域的数学,数学家通常衡量他们亲近鄂尔多斯通过分析鄂尔多斯的令人惊讶的是大型和健壮的合著网络网站(见http://www.oakland.edu/enp/)。保罗的与众不同、引人入胜的故事鄂尔多斯作为一个天才的数学家,才华横溢的problemsolver,掌握合作者提供了许多书籍和在线网站(如。,http://www-history.mcs.st-and.ac.uk/Biographies/Erdos.html)。也许他流动的生活方式,经常住在带着合作者或居住,并给他的钱来解决问题学生奖,使他co-authorships蓬勃发展并帮助构建了惊人的网络在几个数学领域的影响力。为了衡量这种影响asErdos生产,有基于网络的评价工具,使用作者和引文数据来确定影响因素的研究,出版物和期刊。一些科学引文索引,Hfactor、影响因素,特征因子等。谷歌学术搜索也是一个好的数据工具用于网络数据收集和分析影响或影响。ICM2014你的团队的目标是分析研究网络和其他地区的影响力和影响社会。你这样做的任务包括:
1)构建networkof Erdos1作者合著者(你可以使用我们网站https://files.oakland.edu/users/grossman/enp/Erdos1.htmlor的文件包括Erdos1.htm)。你应该建立一个合作者网络Erdos1大约有510名研究人员的文件,与鄂尔多斯的一篇论文的合著者,他但不包括鄂尔多斯。这将需要一些技术数据提取和建模工作获得的节点correctset(鄂尔多斯合作者)和他们的链接(彼此连接ascoauthors)。有超过18000行Erdos1的原始数据文件,但是很多人不会用因为它们链接Erdos1网络之外的人。如果有必要,你可以限制你的网络的规模分析,以校准你的影响力度量算法。一旦建立,分析该网络的属性。(不包括鄂尔多斯——他是最有影响力的,将连接到网络中的所有节点。在这种情况下,它的co-authorship营造网络与他,但他不属于网络或分析。)
2)开发影响措施(s)决定谁在这个Erdos1网络在网络中有显著的影响。考虑谁发表了重要的作品在Erdos1或连接重要人员。同样,假设没有鄂尔多斯扮演这些角色。
3)另一种类型的影响测量)比较研究论文通过分析的意义重要的作品,从其出版。选择一些新兴领域的基础性文件网络科学从附表(NetSciFoundation.pdf)或论文你发现。使用这些文件来分析和开发一个模型来确定它们的相对影响力。构建的影响(合著者或引用)网络和计算分析适当措施。论文在你设定你认为是最具影响力的网络科学,为什么? 有类似的方式来确定个体的作用或影响测量网络研究员? 考虑如何测量作用、影响或影响特定大学的部门,或在网络科学杂志吗? 讨论开发这些措施和方法需要收集的数据。
4)一组完全不同的网络上实现算法影响的数据——例如,影响力的作曲家,音乐乐队,表演者,电影演员、导演、电影、电视节目、专栏作家、记者、报纸、杂志、小说,小说,博客,推特,或者任何你愿意分析的数据集。您可能希望限制网络特定类型或地理位置或预定的大小。
5)最后,讨论科学、理解和建模的影响和影响在网络的效用。可以个人、组织、国家和社会使用影响方法改善人际关系,做生意,和做出明智的决定吗? 例如,在个体层面,描述如何使用你的措施和算法选择谁试图与合著者为了尽快提高你的数学的影响。或你如何使用你的模型和结果来帮助决定毕业学校或导师的选择为你的未来学术工作吗?
6)写报告解释您的建模方法,基于网络的影响和影响的措施,和之前的五项任务的进程和结果。报告不能exceed20页(不包括你的汇总表),应该提供确凿的网络数据的分析,优势,劣势,和灵敏度的方法,建模这些现象使用网络科学的力量。
你的提交应该由一个1页汇总表和您的解决方案不能超过20页最长21页。
这是一个可能的论文清单,可以包含在一组基本的有影响力的网络科学出版物。网络科学是一个新的、新兴、多样化、跨学科领域所以没有大型、集中组易于使用找到的期刊网络报纸,尽管一些新的期刊最近网络科学的建立和新的学术项目正开始在世界各地被提供在大学。您可以使用其中的一些文件或其他你的选择你的团队的设置来分析和比较影响或影响在网络科学任务# 3。
Erd?s, P. and Rényi, A., OnRandom Graphs, Publicationes Mathematicae, 6: 290-297, 1959.
Albert, R. and Barabási, A-L.Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47-97,2002.
Bonacich, P.F., Power and Centrality:A family of measures, Am J. Sociology. 92: 1170-1182, 1987.
Barabási, A-L, and Albert, R.Emergence of scaling in random networks. Science, 286:509-512, 1999.
Borgatti, S. Identifying setsof key players in a network. Computational and Mathematical OrganizationTheory, 12: 21-34, 2006.
Borgatti, S. and Everett, M.Models of core/periphery structures. Social Networks, 21:375-395, October 2000
Graham, R. On properties of awell-known graph, or, What is your Ramseynumber? Annals of the New York Academyof Sciences, 328:166-172, June 1979.
Kleinberg, J. Navigation in asmall world. Nature, 406: 845, 2000.
Newman, M. Scientificcollaboration networks: II. Shortest paths, weightednetworks, and centrality.Physical Review E, 64:016132, 2001.
Newman, M. The structure ofscientific collaboration networks. Proc. Natl.Acad. Sci. USA, 98: 404-409,January 2001.
Newman, M. The structure andfunction of complex networks. SIAM Review,45:167-256, 2003.
Watts, D. and Dodds, P.Networks, influence, and public opinion formation. Journal of ConsumerResearch, 34: 441-458, 2007.
Watts, D., Dodds, P., andNewman, M. Identity and search in social networks. Science, 296:1302-1305, May2002.
Watts, D. and Strogatz, S.Collective dynamics of `small-world‘ networks. Nature, 393:440-442, 1998.
Snijders, T. Statisticalmodels for social networks. Annual Review of Sociology, 37:131–153, 2011.
Valente, T. Social networkthresholds in the diffusion of innovations, Social Networks, 18: 69-89, 1996.