主 题: Faster Random Walks By Rewiring Online Social Networks On-The-Fly
报告人: Prof. Zhiguo Gong (University of Macau)
时 间: 2013-10-10 15:00-16:00
地 点: 二教527（主持人：孙猛）
摘要： Many online social networks feature restrictive web interfaces which only allow the query of a user’s local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long “mixing time”) for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by “rewiring” the social network on-the-fly. Specifically, we develop Modified Topology (MTO)-Sampler which, by using only information exposed by the restrictive web interface, constructs a “virtual” overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTO-Sampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over real-world online social networks such as Google Plus, Epinion etc. 报告人简介： Zhiguo Gong received the B.S. degree in mathematics from Hebei Normal University, Shijiazhuang, China, in 1983, the M.S. degree in mathematics from Peking University, Beijing, China, in 1988, and the Ph.D. degree in Computer Science, Institute of Mathematics, Chinese Academy of Sciences, Beijing, in 1998. He is currently an Associate Professor in the Department of Computer and Information Science, University of Macau, Macau, China. His research interests include databases, Web information retrieval, and Web mining.