中央研究院 資訊科學研究所

Social influence is deeply weaved into the fabric of human society and affects every aspect of human life. Computational social influence is aimed at empowering social influence with computational tools such as modeling, algorithm design, and data mining. In this talk, I will focus on the study of influence diffusion dynamics and the influence maximization problem, which is the problem of selecting a small number of seed nodes in a social network such that their influence coverage after the influence diffusion process is maximized. I will first present our recent results on designing scalable influence maximization algorithms for the classical diffusion models such as the independent cascade model and the linear threshold model, and then show how to extend these models to cover more complex influence diffusion dynamics, such as emergence and propagation of negative opinions, competing influence diffusion, and diffusion on networks with distrust relationships, and how to achieve efficient influence maximization in these models. I will conclude the talk with some discussions on future directions in computational social influence.

 

Wei Chen is a Lead Researcher of Theory Group at Microsoft Research Asia, Beijing, China. He is also an Adjunct Professor at Tsinghua University. His research interests include computational and game theoretic aspects of social networks, algorithmic game theory, distributed computing, and fault tolerance. He won the prestigious William C. Carter Award in 2000 in the area of dependable computing, for his seminal dissertation work on the quality of service of failure detectors. His co-authored paper on a novel game-theoretic approach for community detection in social networks won the best student paper award in ECML PKDD 2009. He has done a series of impactful work on social influence dynamics and social influence maximization, which appeared in recent KDD, ICDM, SDM, WSDM, ICWSM, and AAAI conferences, with collective citation count over 350 in three years.