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学术界与产业界的相关研究课题

Mining Research Topic-related Influence between Academia and Industry
课程网址: http://videolectures.net/ecmlpkdd2011_tang_mining/  
主讲教师: Wenbin Tang
开课单位: 清华大学
开课时间: 2011-10-03
课程语种: 英语
中文简介:
近年来,矿业社会影响问题引起了人们的广泛关注。对于一个社交网络,研究人员对诸如影响、想法、信息如何在网络中传播等问题很感兴趣。在合作作者网络上也提出了类似的问题,其目的是区分社会对研究课题水平的影响,并量化影响的强度。在这项工作中,我们感兴趣的是挖掘学术界和工业界之间的特定话题的影响问题。更具体地说,在一个合作作者网络中,我们想确定哪位学术研究人员在特定的研究主题上对特定公司最具影响力。考虑到研究者之间的相互影响,我们提出了三种模型(简单加性模型、加权加性模型和基于聚类的加性模型)来评估研究者对公司的影响。最后,说明了这三种模型在实际大数据集和模拟数据集上的有效性。
课程简介: Recently the problem of mining social influence has attracted lots of attention. Given a social network, researchers are interested in problems such as how influence, ideas, information propagate in the network. Similar problems have been proposed on co-authorship networks where the goal is to differentiate the social influences on research topic level and quantify the strength of the influence. In this work, we are interested in the problem of mining topic-specific influence between academia and industry. More specifically, given a co-authorship network, we want to identify which academia researcher is most influential to a given company on specific research topics. Given pairwise influences between researchers, we propose three models (simple additive model, weighted additive model and clustering-based additive model) to evaluate how influential a researcher is to a company. Finally, we illustrate the effectiveness of these three models on real large data set as well as on simulated data set.
关 键 词: 计算机科学; 网络分析; 社交网络
课程来源: 视频讲座网
最后编审: 2021-01-29:nkq
阅读次数: 31