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- October 5, 2012 2:00 pm - 3:00 pm
- Klaus 2447
Estimating heterogeneous social influence
Information, disease, and influence diffuse over networks of entities in both natural systems and human society. Analyzing these transmission networks plays an important role in understanding the diffusion processes and predicting future events. However, the underlying transmission networks are often hidden and incomplete, and we observe only the time stamps when cascades of events happen. In this talk, I will address the challenging problem of uncovering the hidden network only from the cascades. This structure discovery problem is complicated by the fact that the influence among different entities in a network is heterogeneous, which can not be described by a simple parametric model. Therefore, I will discuss a kernel-based method which can capture a diverse range of different types of influence without any prior assumption. In both synthetic and real cascade data, this new method can better recover the underlying diffusion networks and drastically improve the estimation of the transmission functions between networked entities.
Le Song is an Assistant Professor in the College of Computing at Georgia Institute of Technology. He works on the area of machine learning, with focus on kernel methods and nonparametric graphical models, and applications to computer vision, computational biology, and social media problems. His previous appointments include Research Scientist at Google Research and Postdoctoral Fellow at Carnegie Mellon University. Dr. Song received his Ph.D. in computer science from the University of Sydney and National ICT Australia in 2008.