Abstract:

We are living in the Internet Age, in which information entities and objects are interconnected, thereby forming gigantic information networks. Examples of real-world information networks include social networks, bibliographic networks, gene regulation and protein interaction networks, knowledge graph, and the World Wide Web. It is critical to quickly process and understand these networks in order to enable data-driven applications. However, there are two main challenges for analyzing big networks. First, modern networks grow and involve over time, we require learning algorithms which are able to work on the fly and are adaptive to the variation of the networks. Second, the labels of the nodes or edges in big networks are scarce, it is urgent to optimize the process by which the labels are collected. In this talk, to address the above challenges, I will present several online and active learning algorithms for big network analytics, which are both statistically and computationally efficient, and with provable guarantee on their performance. Empirical studies on real-world networked data validate the effectiveness of the proposed algorithms.

Bio:

Quanquan Gu is a Ph.D. candidate in Department of Computer Science, University of Illinois at Urbana-Champaign, supervised by Prof. Jiawei Han. He received his MS and BS degrees in Tsinghua University, China. He is the recipient of IBM PhD Fellowship for 2013-2014. His main research interests include theory and algorithms for big data analytics and machine learning, with focus on networked data.