Abstract
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. The speaker formalizes 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, he is interested in social ties that do not follow from homophily, thus, provide new insights. Finding top-k GRs faces new challenges. Firstly, it requires a novel ranking metric because traditional metrics favor patterns that are expected from the homophily principle; secondly, it requires an innovative search strategy since there is no obvious anti-monotonicity for such GRs; thirdly, 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. In this lecture, the speaker will address these issues presenting an efficient algorithm, GRMiner, for mining top-k GRs and evaluate its effectiveness and efficiency using real data.
About the speaker
Prof Ke Wang received his MSc and PhD in Information and Computer Science from Georgia Institute of Technology in 1984 and 1986 respectively. He moved to Chongqing University as an Associate Professor in 1987 and joined National University of Singapore as a research fellow in 1992, where he was eventually promoted to an Associate Professor in 1999. In 2000, Prof Wang moved to Simon Fraser University and has been the Professor of Computing Science since 2003.
Prof 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.
Prof Wang is currently an associate editor of the ACM Transactions on Knowledge Discovery from Data journal and an associate editor of the IEEE Transactions on Knowledge and Data Engineering journal. He is also 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 a general co-chair for the SIAM Conference on Data Mining 2016.
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