Adaptive Diffusions for Scalable and Robust Learning over Graphs

Prof. Georgios Giannakis, McKnight Presidential Endowed Chair, University of Minnesota

Wegmans Hall 1400

Abstract:Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however depends on the extent to which the chosen diffusion captures a typically unknown label propagation mechanism that can be specific to the underlying graph, and potentially different for each class. This talk will introduce a disciplined, data-efficient approach to learning class-specific diffusion functions adapted to the underlying network topology. The novel learning approach leverages the notion of “landing probabilities” of class-specific random walks, which can be computed efficiently, thereby ensuring scalability to large graphs. Furthermore, a robust version of the classifier becomes available for graph- aware learning even in noisy environments. Classification tests on real networks will demonstrate that adapting the diffusion function to the given graph and observed labels, markedly improves the performance over fixed diffusions; reaching – and many times surpassing – the classification accuracy of computationally heavier state-of-the-art competing methods, that rely on node embeddings and deep neural 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 U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, 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 450 journal papers, 750 conference papers, 25 book chapters, two edited books and two research monographs (h-index 143). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.