Social networks are pervasive and encompass interactions over online social media, human links in epidemic processes, terrorist cells, and collaborations among researchers. The value in understanding and predicting complex network behavior cannot be understated, thanks to the growing role of search engines, cyber warfare, online marketing, and social recommendation tools. 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.

 

Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the Univ. of Minnesota, where he holds a Chair in Wireless Telecommunications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center.  His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 400 journal papers, 700 conference papers, 25 book chapters, two edited books and two research monographs (h-index 127).

 

Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 30 patents issued, and the (co-) recipient of 8 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the inaugural IEEE Fourier Technical Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SP Society.