主讲人简介:

Dr. Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include graph mining, graph query processing, graph pattern matching, keywords search in databases, and online social networks. Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), and an associate editor in VLDB Journal (2007-2013). Currently he serves as an associate editor of ACM Transactions on Database Systems (TODS), WWW Journal, Data Science and Engineering, the International Journal of Cooperative Information Systems, the Journal on Health Information Science and Systems (HISS), and Journal of Information Processing. Dr. Yu served/serves in many organization committees and program committees in international conferences/workshops including PC Co-chair of APWeb'04, WAIM'06, APWeb/WAIM'07, WISE'09, PAKDD'10, DASFAA'11, ICDM'12, NDBC'13, ADMA'14, CIKM'15 and Bigcomp17, and conference general Co-chair of PWeb'13 and ICDM'18.

讲座摘要:

Graph has been widely adopted to model complex networks. Finding hidden structures in massive graphs is an important issue.For online social networks, there are two interesting issues: social hierarchy and social communities.Social hierarchy is a fundamental concept in sociology and social network analysis. The importance of social hierarchy in a social network is that the topological structure of the social hierarchy is essential in both shaping the nature of social interactions between individuals and unfolding the structure of the social networks.Social Community structures, as functional building blocks, exist in social networks.Thus, mining and querying community structure in large networks becomes an important issue for a deeper understanding and better management of such networks.In this talk, we will discuss several recent work on searching communities in large networks and on finding the hierarchy structure in large social networks.