20160327大规模社交网络上的推断与学习Inference and Learning over Large-Scale Social Networks
Georgios B. Giannakis is a professor with the University of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing. He is a Fellow of the IEEE and EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SPS. Real-world social networks are fraught with unique challenges that limit the efficacy of contemporary tools. For example, such networks are big (billions of nodes), evolve over time, and are often not directly observable. Viewed through a statistical learning lens, many network analytics problems boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting this point of view, this talk will put forth novel learning approaches for network visualization, anomaly and community detection, prediction of network processes, and dynamic network inference. Key emphasis will be placed on parsimonious models exploiting sparsity, low rank, or low-dimensional manifolds, attributes that have been shown useful for complexity reduction. The merits of the novel schemes will be demonstrated on both simulated and real-world social networks.