GlobalSIP 2018:

Graph Signal Processing

[Download the PDF Call for Papers]

Understanding networks and networked behavior has emerged as one of the foremost intellectual challenges of the 21st century. Networks define an underlying notion of proximity and the main object of interest is a signal defined on top of the graph, i.e., data associated with the nodes or edges of the network. This is precisely the focus of graph signal processing, studying the interplay between the underlying network topology and features of signals defined on networks. This symposium aims to bring together researchers and practitioners of graph signal processing to discuss the latest advances in theory, methods, and applications, as well as open problems and challenges.

Distinguished Symposium Talks

Georgios Giannakis Photo

Georgios Giannakis

University of Minnesota

Online Scalable Learning Adaptive to Unknown Dynamics and Graphs

Kernel based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation, this talk will introduce first for static setups a scalable multi-kernel learning approach (termed Raker) to obtain the sought nonlinear learning function ‘on the fly,’ bypassing the `curse of dimensionality’ associated with kernel methods. We will also present an adaptive multi-kernel learning scheme (termed AdaRaker) that relies on weighted combinations of advices from hierarchical ensembles of experts to boost performance in dynamic environments. The weights account not only for each kernel’s contribution to the learning process, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, with analytic performance guarantees. The approach is further tailored for online graph-adaptive learning with scalability and privacy. Tests with synthetic and real datasets will showcase the effectiveness of the novel algorithms.

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 430 journal papers, 720 conference papers, 25 book chapters, two edited books and two research monographs (h-index 133). 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.

Michael Rabbat Photo

Michael Rabbat

Facebook Research

Learning graphs from data

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the datasets, it is desirable to infer or learn a graph topology from the data. I will discuss approaches to the problem of graph learning, emphasizing those that adopt a graph signal processing perspective.

Michael Rabbat earned the B.Sc. from the University of Illinois, Urbana-Champaign, in 2001, the M.Sc. from Rice University, Houston, TX, in 2003, and the Ph.D. from the University of Wisconsin, Madison, in 2006, all in electrical engineering. He is currently a Research Scientist with Facebook AI Research. From 2007 - 2018 he was a professor in the Department of Electrical and Computer Engineering at McGill University. He serves as an Associate Editor for IEEE Transactions on Signal and Information Processing over Networks, as an elected member of the IEEE SPS Big Data SIG, and as a member of the IEEE SPS Data Science Initiative steering committee. He previously served as Associate Editor for IEEE Transactions on Control of Networked Systems, and as Senior Area Editor for IEEE Signal Processing Letters. His research interests include distributed algorithms for optimization and inference, and graph signal processing.


Tuesday, November 27
09:40 - 10:40
DL DL-GSP.1: Georgios Giannakis: "Online Scalable Learning Adaptive to Unknown Dynamics and Graphs"
11:00 - 12:30
GSP-L.1: Graph Signal Processing I
14:00 - 15:30
GSP-L.2: Graph Signal Processing II
15:50 - 16:50
DL DL-GSP.2: Michael Rabbat: "Learning graphs from data"
17:00 - 18:00
GSP-P.1: Graph Signal Processing III

Organizing Committee

General Chair

Technical Program Chairs

Submissions are welcome on topics including:

Paper Submission

Prospective authors are invited to submit full-length papers (up to 4 pages for technical content including figures and possible references, and with one additional optional 5th page containing only references) and extended abstracts (up to 2 pages, for paper-less industry presentations and Ongoing Work presentations).. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Accepted full-length papers will be indexed on IEEE Xplore. Accepted abstracts will not be indexed in IEEE Xplore, however the abstracts and/or the presentations will be included in the IEEE SPS SigPort. Accepted papers and abstracts will be scheduled in lecture and poster sessions.

Important Dates

Paper Submission DeadlineJune 17, 2018 June 29, 2018
Review Results AnnouncedSeptember 7, 2018
Camera-Ready Papers DueSeptember 24, 2018
November 5, 2018Hotel Room Reservation Deadline