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美国阿肯色大学徐晓伟教授《Structural Pattern Mining from Big Network Data》
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报告题目:《Structural Pattern Mining from Big Network Data》

报告地点:C1-312

报告时间:2015年7月14日(周二),下午3点

报告人:徐晓伟

报告人单位:美国阿肯色大学

面向听众:学院教师;一、二年级硕士生。

报告人简介:

    徐晓伟:美国阿肯色大学小石城分校信息科学系教授,兼任数学系教授;曾任美国联邦食品和药物管理局国家毒理学实验室(National教授。1983年在南开大学数学系获得学士学位,1987年在中国科学院沈阳计算技术研究所获得硕士学位,1998年在德国慕尼黑大学(University of Munich)获得博士学位。1998年~2002年在西门子公司任高级研究科学家; 自2012年任中国科学院沈阳自动化研究所客座研究员,博士生导师; 同时兼任东北大学客座教授。曾经是香港中文大学访问教授;为多家国际公司提供咨询服务,其中包括西门子公司,Axciom 公司,Dataminr 公司 和东软公司。多次在模式识别和数据挖掘领域知名国际会议上作大会特邀报告,在国际知名学术期刊和国际会议上发表了具有原创性的研究成果。他提出的基于密度的聚类等一系列理论算法,具有理论原创性,并被写入教科书。最近徐晓伟教授获得美国计算机协会ACM SIGKDD Test of Time 奖,表彰其在基于密度聚类算法的研究对数据挖掘领域所产生的重要影响。

报告内容简介:

  Networks are ubiquitous in our world. Prominent examples are the WWW and social networks. Many of the networks are very big and complex consisting of over millions of nodes and links. Therefore, pattern mining from big networks is a daunting task. In this talk we focus on mining two kinds of structural patterns including community structures and functional roles of nodes. More specifically, community structures are densely connected groups of nodes, with only sparser connections between groups. An example of community structures in social networks is a group of like-minded people. Many algorithms find community structures. But they tend to fail to identify and isolate two kinds of nodes that play special roles – nodes that bridge communities (hubs) and nodes that are marginally connected to communities (outliers). Recently, we proposed a novel algorithm called SCAN (Structural Clustering Algorithm for Networks), which detects community structures, hubs and outliers in networks. The algorithm is fast, visiting each node only once. An empirical evaluation of the method using both synthetic and real datasets demonstrates superior performance over other methods such as the modularity-based algorithms. Last but not least, we present a MapReduce/Hadoop implementation of SCAN for big social networks like Twitter