Abstract: Graph analytics, especially for the big graphs with billions of nodes and edges, is an exciting new area for big data analytics workloads. It has drawn enormous research interests, not only because many complicated real-world big data, such as social data, financial data, transportation data, crime data, biological data, etc., can be modeled as graphs, but also because the graph analytics can reveal underlying patterns in these data from the perspective of relationship or topology. In this tutorial, I will review the recent work of my group in both academic research and industrial application, including geo-social network analytics, diversified service composition based on graphs, attributed network embedding, and financial graph analytics.

Biography: Dr. Ming Zhong is currently an associate professor at School of Computer Science, Wuhan University, and is the deputy director of Hubei Provincial Engineering Research Center in Public Finance and Economy Big Data. He received his PhD and BD in Computer Science from Wuhan University, and worked at New York State University at Stony Brook, USA and Carleton University, Canada as visiting research fellow. His research interests include big data analytics, graph analytics, fintech, etc. He published over thirty research papers of the related topics, and was funded by NSFC, NSF of Hubei Province, Doctoral Fund of Ministry of Education, etc.