Leveraging the Power of Social Propagations: Algorithm Designs for Social Marketing
发布时间:2019-07-15 浏览:229

报告题目:Leveraging the Power of Social Propagations: Algorithm Designs for Social Marketing

报告人:杨禹 博士





Social propagation is a fundamental and prevalent process taking place in social networks, where due to the peer influence of social network users, behaviors of a few influential users can spread widely in a network. Leveraging the social propagation effect lies at the core of mining marketing values of social networks, and has been studied extensively in the past decade. Most studies aimed at identifying influential users in a given social network, the first step of making good use of propagation in social marketing. However, in many deeper marketing applications, such as the real-time recommendation of influential users and personalized pricing in promotional campaign planning, effectively mining social propagations faces many unsettled and challenging computational problems. In this talk, I will introduce some of our recent progress in algorithmic methods for exploiting social propagations in marketing. We investigated three crucial problems along this line: (1) how to efficiently monitor top influential users in a fast-evolving social network; (2) how to cost-effectively motivate influential users to trigger a large-scale propagation for our marketing purposes; (3) how to maximize social network users’ interaction/ discussion on a topic by scheduling an effective information diffusion. Our work provides powerful algorithmic tools to solve these problems effectively, which at the same time are efficient and can deal with large networks containing millions or even tens of millions of vertices in a single machine. I will conclude by discussing some future directions in mining social propagations.


Yu Yang is currently an Assistant Professor with the School of Data Science at the City University of Hong Kong. His research interests lie in the algorithmic aspects of data mining and data science, with an emphasis on managing and mining dynamics of large-scale networks. His work appears in premier venues such as SIGMOD, VLDB, ICDE, IJCAI, TKDE, TKDD, and KAIS. He obtained his Ph.D. in Computing Science from the Simon Fraser University in Feb. 2019. Before that, he received his B.E. degree from the Hefei University of Technology in 2010, and his M.E. degree from the University of Science and Technology of China in 2013, both in Computer Science.