报告主题：From Graph Sampling to Network Structural Modeling
报告人：LU Xuesong, Ph.D
时间：2018-03-09 13:00--1400
地点：中山北路3663号华东师范大学数学馆东109室
报告内容：
One fundamental challenge of managing large graph data is the difficulty of applying algorithms on large-scale graphs due to the high computational complexity of the graph algorithms, especially when the sizes of today’s real-life graphs are becoming unprecedentedly massive. Rather than scale up existing algorithms to cater for the increasingly large sizes of graphs, an alternative strategy is to sample from large graphs and extract representative subgraphs of manageable sizes for the algorithms. In this talk, I first introduce two novel graph sampling techniques that can handle static graphs and dynamically growing graphs, respectively. I compare them with the state-of-the-art algorithms and show the superiority of the proposed techniques.
Network modeling is an important method for studying how the structures of networks can influence various business outcomes, e.g., whether an advertising strategy to the members of a social network yields optimal revenue for a new product. In the second part of the talk, I introduce a working project that attempts to model how the similarity or difference of friends’ reservation prices for a product should affect the optimal price and advertising level for that product, in the context of word-of-mouth (WOM) networks and the correlation of friends’ preferences (network assortativity).
个人简介：
LU Xuesong is a research fellow of Information Systems and Analytics at School of Computing at National University of Singapore. He received his Bachelor degree in computer science from Fudan University in 2008, and his PhD in computer science from National University of Singapore in 2013.
During his PhD candidate, LU Xuesong was working on random sampling and generation over data streams and large graphs. The work aimed to extract representative sub-datasets of manageable sizes from large-scale datasets. After graduating from NUS, he joined the DIAS Lab at EPFL in Switzerland and worked on spatial data management under the Human Brain Project, particularly on indexing large-scale spatial data. Then he joined PPDai in Shanghai and worked as a senior data scientist. He was focused on business optimization through data-driven modeling.
After coming back to NUS, LU Xuesong’s work has been focusing on structural modeling and big data analytics using data mining/machine learning techniques.
