2014 ICM Problem 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. 衡量学术研究影响的方法，看引用和论文合作的网络。 Co-authoring a manuscript usually connotes a strong influential connection between researchers. 论文合作的含义 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. 举一个例子 PAUL ERDOS 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. 可能 ERDOS 还是建立网络科学基础的人 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 http:/www.oakland.edu/enp/ ). 一个衡量论文合作的 network 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). 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. 可能他的生活方式推动了他的合作网的发展。 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 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 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). 第一，建立一个网络，衡量一下 510 个人之间的联系。到底网络是反应什么？反应共 同发表或者相互引用？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.) 1、建立一个网络，把 erdos1 的作家全部起来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. 建立衡量影响的测度方法，决定谁是 erdos1 network 里最重要的人，考虑谁出版了重要工 作或者与 erdos1 里的重要人物有影响（是不是需要反馈？） 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. 又回来建立文章的重要性？这个比起来第二问里衡量人的重要性复杂在哪里？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 t