Thursday, December 8 | |
10:00 - 11:00 | |
Keynote: SPN-K1: Antonio Ortega - Learning Graphs from Data | |
11:00 - 12:20 | |
SPN-1: Signal and Information Processing Over Networks I | |
14:00 - 15:40 | |
SPN-2: Signal and Information Processing Over Networks II | |
16:10 - 17:30 | |
SPN-P1: Signal and Information Processing Over Networks Poster I | |
Friday, December 9 | |
10:00 - 11:00 | |
Keynote: SPN-K2: Alejandro Ribeiro - Statistical Signal Processing on Graphs | |
11:00 - 12:20 | |
SPN-3: Signal and Information Processing Over Networks III | |
14:00 - 15:40 | |
SPN-4: Signal and Information Processing Over Networks IV | |
16:10 - 17:30 | |
SPN-P2: Signal and Information Processing Over Networks Poster II |
There has been significant recent progress in the development of tools for graph signal processing, including methods for sampling and transforming graph signals. In many applications, a graph needs to be learned from data before these graph signal processing methods can be applied. A standard approach for graph learning is to estimate the empirical covariance from the data and then compute an inverse covariance (precision) matrix under desirable structural constraints. We present recent results that allow us to solve these problems under constraints that encompass a broad class of generalized graph Laplacians. These methods are computationally efficient, can incorporate sparsity constraints, and can also be used to optimize weights for a given known topology. We illustrate these ideas with examples in image processing and other areas.
A network can be understood as a complex system formed by multiple nodes, where global network behavior arises from local interactions between connected nodes. Often, networks have intrinsic value and are themselves the object of study. In other occasions, the network defines an underlying notion of proximity or dependence, but the object of interest is a signal defined on top of the graph. This is the matter addressed in the field of graph signal processing (GSP). Graph-supported signals appear in many engineering and science fields such as gene expression patterns defined on top of gene networks and the spread of epidemics over social networks. Transversal to the particular application, the philosophy behind GSP is to advance the understanding of network data by redesigning traditional tools originally conceived to study signals defined on regular domains and extend them to analyze signals on the more complex graph domain. In this talk, we will introduce the main building blocks of GSP and illustrate the utility of these concepts through real-world applications. Our focus will be on the definition of stationary graph signals and the inference of underlying graph structures from graph signal observations.
Since its first edition in 2013, GlobalSIP has rapidly assumed flagship status within the IEEE Signal Processing Society. The conference is comprised of co-located symposia focused on signal and information processing and up-and-coming signal processing themes. GlobalSIP aims to feature world-class speakers, tutorials, exhibits, and oral and poster sessions. Prospective contributors to the symposium on ``Signal and Information Processing Over Networks'' are invited to submit works that use signal and information processing tools to understand networks and networked behavior, which has emerged as one of the foremost intellectual challenges of the 21st century. Often, networks have intrinsic value and are themselves the object of study. In other instances, the network defines 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 of the network. Under the assumption that the signals are related to the topology of the graph where they are supported, the goal is to develop innovative signal and information algorithms that fruitfully leverage this relational structure, and can make inferences about these relationships when they are only partially observed. The list of applications is vast and spans a wide spectrum of disciplines such as biology, sociology, economics, engineering, or computer science.
Researchers working on interdisciplinary fields and those from communities related to Signal Processing are encouraged to submit their results.
Submissions are welcome on topics including:
[Download the PDF Call for Papers]
Prospective authors are invited to submit Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Submission is through the GlobalSIP website at http://2016.ieeeglobalsip.org/Papers.asp.
Paper Submission Deadline | |
Review Results Announced | |
Camera-Ready Papers Due | September 30, 2016 |