2014ICM中英对照.doc

2014 ICM Problem （红色是我认为的关键点）Using Networks to Measure Influence and Impact 使用网络来测量影响和冲击One of the techniques to determine influence of academic research is to build and measure properties of citation or co-author networks. 建立和衡量引用文献（或合著者网络）的属性是决定学术研究影响的其中一项技术。Co-authoring a manuscript usually connotes a strong influential connection between researchers. 研究者共同合作撰写一份手稿通常意味着他们之前有着强而有力的关系（连结）。One of the most famous academic co-authors was the 20th-century mathematician Paul Erds who had over 500 co-authors and published over 1400 technical research papers.其中，最出名的学术合著者要数20世纪的数学家Paul Erdos，一个与超过500合著者合作过并且已经出版超过1400 技术性研究论文的人。It is ironic, or perhaps not, that Erds is also one of the influencers in building the foundation for the emerging interdisciplinary science of networks, particularly, through his publication with Alfred Rnyi of the paper “On Random Graphs” in 1959. 讽刺的是，（或者本来就是）,Erdos 也是其中一个在建立新兴交叉科学网络基础上的影响者，尤其是他与Alfred Rny 合著并在1959年出版的刊物“On Random Graphs(关于随机图)”。Erdss role as a collaborator was so significant in the field of mathematics that mathematicians often measure their closeness to Erds through analysis of Erdss amazingly large and robust co-author network (see the website /enp/ ).作为一个合作者，在数学领域Erdos的存在显得如此重要，数学家经常通过Edros惊人庞大和健全的合著者网络来衡量他们与Erdos距离（见网站/enp/）。The unusual and fascinating story of Paul Erds as a gifted mathematician, talented problem solver, and master collaborator is provided in many books and on-line websites (e.g., http:/www-history.mcs.st-and.ac.uk/Biographies/Erdos.html ). 天生的数学家、天才的问题解决者、擅长的合作者Paul Erdos有着不寻常和令人着迷的故事，这些故事在许多书籍和在线网站都有提供(e.g., http:/www-history.mcs.st-and.ac.uk/Biographies/Erdos.html)。Perhaps his itinerant lifestyle, frequently staying with or residing with his collaborators, and giving much of his money to students as prizes for solving problems, enabled his co-authorships to flourish and helped build his astounding network of influence in several areas of mathematics. 也许由于他不固定的生活方式，经常与他的合作者同住在一起，并给了许多钱作为奖金给了他的学生们去解决问题，使他的合作者蓬勃发展并帮助他在数学的各个领域建立起有惊人影响力的网络。In order to measure such influence as Erds produced, there are network-based evaluation tools that use co-author and citation data to determine impact factor of researchers, publications, and journals. 为了衡量Erdos所产生的影响，这里有一个使用共同作者和引文数据的基于网络的评估工具，用来确定研究人员，出版物和期刊的影响因素。Some of these are Science Citation Index, H-factor, Impact factor, Eigenfactor, etc. Google Scholar is also a good data tool to use for network influence or impact data collection and analysis. 其中有些是科学引文索引，H因子，影响因子，特征因子等。例如：在网络影响或者数据收集影响和分析方面，谷歌学术搜索器也是一个很不错的数据工具。Your teams goal for ICM 2014 is to analyze influence and impact in research networks and other areas of society. Your tasks to do this include: 你们团队在ICM 2O14要做的是： 去分析研究网和其它社会领域的影响力和冲击力。你们要做的任务包括：1) Build the co-author network of the Erdos1 authors (you can use the file from the website /users/grossman/enp/Erdos1.htmlor the one we include at Erdos1.htm). 构建“埃数1合著者”网络（您可以使用网站上的文件/users/grossman/enp/Erdos1.html或 包含在Erdos1.htm ）。You should build a co-author network of the approximately 510 researchers from the file Erdos1, who coauthored a paper with Erds, but do not include Erds. 建立的一个约有510个研究人员的合著者网络，选择的人是与Erdos有过合著论文的研究人员（不包括Erdos）.这些人员在文件Erdos1里。This will take some skilled data extraction and modeling efforts to obtain the correct set of nodes (the Erds coauthors) and their links (connections with one another as coauthors). 这将需要一些提炼过的数据和模拟研究以获得正确的节点（埃尔德什合著者）和他们的链接（彼此作为合著者之间的连接）。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.在Erdos1文件中有超过18,000个原始数据行，但很多原始数据行不会被用到，因为它们是链接到Erdos1网络之外的人。If necessary, you can limit the size of your network to analyze in order to calibrate your influence measurement algorithm. 如果可以，为了校准你的影响力测量算法，你可以限制您的网络的大小（limit size是缩小范围）来进行分析。Once built, analyze the properties of this network. (Again, do not include Erds - he is the most influential and would be connected to all nodes in the network. In this case, its co-authorship with him that builds the network, but he is not part of the network or the analysis.) 一旦建成这个网络，分析这个网络的性能（属性，特质）。（同样，不包括埃尔德什他是最有影响力的，并会被连接到网络中的所有节点。在这种情况下，与他有过合作著作的人组成了这个网络，但他不是这个网络或分析的一部分。）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 Erds is not there to play these roles. 深化影响力(衡量)方法，以确定在这个Erdos1网络里，谁具有最显著的影响力。考虑谁曾发表重要作品或连接着Erdos1里的重要研究人员。同样，假设埃尔德什没有扮演这些角色。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. 另外一种测量影响力的方法是通过分析这些出版的重要的作品来比较research paper的重要性。Choose some set of foundational papers in the emerging field of network science either from the attached list (NetSciFoundation.pdf) or papers you discover. 在网络科学的新兴的网络科学领域从附表（ NetSciFoundation.pdf ）或你发现的论文中选择一些基础性的论文来组成论文集。Use these papers to analyze and develop a model to determine their relative influence. 使用这些论文集来分析和建立一个模型来确定它们的相对影响力。Build the influence (coauthor or citation) networks and calculate appropriate measures for your analysis. 建立（合著者或引文的）影响力网络，并为你的分析算出一个合适方法。Which of the papers in your set do you consider is the most influential in network science and why? 在你设定的论文集里，你认为哪一些在网络科学里是最有影响力的，为什么？Is there a similar way to determine the role or influence measure of an individual network researcher? 是否有一个类似（统一）的方法来确定个体网络研究人员的在网络中的作用或影响方式？Consider how you would measure the role, influence, or impactof a specific university, department, or a journal in network science? 考虑一下如何去衡量一个具体的高校、部门或期刊在网络科学里的角色、影响力或作用 ？(Impact 有的地方看做影响，有的地方为冲击力。一般情况下视作影响。因为无论是冲击力或者影响力其实侧重都在于带来怎样的后果，结果：就是result。只要对他的结果做分析即可)Discuss methodology to develop such measures and the data that would need to be collected. 讨论一下用什么方法来发展这样的措施，以及需要被收集的数据。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. 对一组完全不同的网络影响力数据例如，有影响力的作曲家，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，小说家，小说，博客，高音喇叭，或你在乎的任何数据集，完成你的算法 。You may wish to restrict the network to a specific genre or geographic location or predetermined size. 您不妨将网络限制到一个特定的风格或地理位置或预定尺寸5) Finally, discuss the science, understanding and utility of modeling influence and impact within networks. 最后，讨论（你所建立的）影响力和冲击力模型的科学性、合理性和有效性。Science：应该是讨论你用了什么样的技术，怎样去建立起来的和过程Understanding应该是讨论你对你所建立的东西的理解。Utility：应该是有效性以及应用到什么方向范围.对这一领域或者人类有什么贡献影响之类。Could individuals, organizations, nations, and society use influence methodology to improve relationships, conduct business, and make wise decisions? 个人、组织、国家和社会是否可以使用（你建立的）影响力的方法来改善关系，开展业务，并下明智的决定？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. 例如，在个人层面上，描述你如何使用你的措施和算法来选择与谁共同创作以尽可能快地提高你在数学领域的影响力。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? 或者你怎么使用你的模型和结果以帮助一个研究生院做决定或论文指导老师选择你的未来学术工作？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. 写一份报告，说明你的建模方法，你的基于网络的影响力和影响措施，对五项任务的进度和结果。The report must not exceed20 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.该报告不得超过20页（不包括您摘要页），并应给出对您的网络数据固定的分析；优势； 弱点，你的方法的灵敏度；用网络科学给出对这些现象建模的效率。*Your submission should consist of a 1 page Summary Sheet and your solution cannot exceed 20 pages for a maximum of 21 pages.*您提交的内容应包括1页摘要表及不 超过20页的解决方案，最多21页。NetSciFoundation.pdfThis is a listing of possible papers that could be included in a foundational set of influential publications in network science. 这是一个可能包括了在网络科学里有影响力的一些出版论文集。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. 网络科学是一个新的、新兴的、多样的、交叉学科领域，所以没有更大的、更浓缩期刊集，很容易的用它来寻找网络方面的论文，即使一些新的杂志最近才明确建立起来，而在世界各地的大学里，关于网络科学新的学术项目都开始将予提呈发售。You can use some of these papers or others of your own choice for your teams set to analyze and compare for influence or impact in network science for task #3.你可以使用一些论文或其他你自己选择的论文作为您的团队论文集，分析和比较任务3在网络科学里的影响或效果。Erds, P. and Rnyi, A., On Random Graphs, Publicationes Mathematicae, 6: 290-297, 1959.Albert, R. and Barabsi, 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.Barabsi, 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 2000Graham, 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, 37:131153, 2011.Valente, T. Social network thresholds in the diffusion of innovations, Social Networks, 18: 69-89, 1996.Erdos1, Version 2010, October 20, 2010This is a list of the 511 coauthors of Paul Erdos, together with their coauthors listed beneath them. 这个列表是511个保罗埃尔德什的合作者，连同它们下面列出他们的合作者。The date of first joint paper with Erdos is given, followed by the number of joint publications (if it is more than one). （该合作者）与Erdos第一次联合paper的日期已经给出，接下来的是（与Erdos的）合作出版物的数量（如果出版物数目大于1）。An asterisk following the name indicates that this Erdos coauthor is known to be deceased; 名字后有星号的表明该埃尔德什的合著者是已故的；additional information about the status of Erdos coauthors would be most welcomed. 这是不是没用的额外的信息关于埃尔德什的合著者的状态是最欢迎的。(This convention is not used for those with Erdos number2, as to do so would involve too much work.) 这个 convention指什么？是不是指那个与Erdos的合作出版物的数量不适用于Erdos number2的人。只适用于endors1（erdos的511个合作者），而不适用于 erdors2（511个合作者的合作者）,这样做将涉及太多的工作。Numbers preceded by carets（） follow the convention used by Mathematical Reviews in MathSciNet to distinguish people with the same names. 人名后面有个（数字）是为了区分该网站内同名的作家。Please send corrections and comments 请把整改意见寄至The Erdos Number Project Web site can be found at the following URL:/enp 埃数项目网站可以在以下网址找到: /enp 2014 ICM Problem （与上面的内容一样只是排版不同）Using Networks to Measure Influence and Impact One of the techniques to determine influence of academic research is to build and measure properties of citation or co-author networks. Co-authoring a manuscript usually connotes a strong influential connection between researchers. One of the most famous academic co-authors was the 20th-century mathematician Paul Erds who had over 500 co-authors and published over 1400 technical research papers.It is ironic, or perhaps not, that Erds is also one of the influencers in building the foundation for the emerging interdisciplinary science of networks, particularly, through his publication with Alfred Rnyi of the paper “On Random Graphs” in 1959. Erdss role as a collaborator was so significant in the field of mathematics that mathematicians often measure their closeness to Erds through analysis of Erdss amazingly large and robust co-author network (see the website /enp/ ). The unusual and fascinating story of Paul Erds as a gifted mathematician, talented problemsolver, and master collaborator is provided in many books and on-line websites (e.g., http:/www-history.mcs.st-and.ac.uk/Biographies/Erdos.html). Perhaps his itinerant lifestyle, frequently staying with or residing withhis collaborators, and giving much of his money to students as prizes for solving problems, enabled his co-authorships to flourish and helped build his astounding network of influence in several areas of mathematics. In order to measure such influence as Erds produced, there are network-based evaluation tools that use co-author and citation data to determine impact factor of researchers, publications, and journals. Some of these are Science Citation Index, H-factor, Impact factor, Eigenfactor, etc. Google Scholar is also a good data tool to use for network influence or impact data collection and analysis. Your teams goal for ICM 2014 is to analyze influence and impact in research networks and other areas of society. Your tasks to do this include: 1) Build the co-author network of the Erdos1 authors (you can use the file from the website /users/grossman/enp/Erdos1.htmlor 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 Erds, but do not include Erds. This will take some skilled data extraction and modeling efforts to obtain the correct set of nodes (the Erds 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 Erds - he is the most influential and would be connected to all nodes in the network. In this case, its 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 Erds 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 exceed20 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 sho