报告题目：Mining Social Ties Beyond Homophily
报告人：Ke Wang（ Professor，School of Computing Science, Simon Fraser University, Canada）
Summarizing patterns of connections or social ties in a social network, in terms of attributes information on nodes and edges, holds a key to the understanding of how the actors interact and form relationships. We formalize this problem as mining top-k group relationships (GRs), which captures strong social ties between groups of actors. While existing works focus on patterns that follow from the well known homophily principle, we are interested in social ties that do not follow from homophily, thus, provide new insights. Finding top-k GRs faces new challenges: it requires a novel ranking metric because traditional metrics favor patterns that are expected from the homophily principle; it requires an innovative search strategy since there is no obvious anti-monotonicity for such GRs; it requires a novel data structure to avoid data explosion caused by multidimensional nodes and edges and many-to-many relationships in a social network. We address these issues through presenting an efficient algorithm, GRMiner, for mining top-k GRs and we evaluate its effectiveness and efficiency using real data.
Ke Wang received Ph.D from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser University. Ke Wang's research interests include database technology, data mining and knowledge discovery, with emphasis on massive datasets, graph and network data, and data privacy. He is particularly interested in combining the strengths of database, statistics, machine learning and optimization to provide actionable solutions to real life problems. Ke Wang is an associate editor of the ACM TKDD journal and he was an associate editor of the IEEE TKDE journal, an editorial board member for Journal of Data Mining and Knowledge Discovery. He is a general co-chair for the SIAM Conference on Data Mining 2015 and 2016.