In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.

We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal.

Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses and the pressure on the memory subsystem. Yet, selecting a proper compression method is challenging as there exist a plethora of techniques, algorithms, domains, and approaches in compressing graphs.

The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work, we study the joint design of graph signal sampling along with quantization, for graph signal compression.

In this work, we propose a compression method for ban-dlimited graph signals which maps a high-dimensional signal into a finite-bit representation via sampling and quantiza-tion. Our compression method is inspired by the recently proposed task …

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings.

Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains David Shuman Ecole Polytechnique F ed erale de Lausanne (EPFL) [email protected] Macalester College February 11, 2013 Special thanks and acknowledgment to my collaborators: Xiaowen Dong, Mohammad Javad …

compressed graph direct processing via rule interpretation. •We develop CompressGraph, a graph analytics engine that can perform efficient compressed graph direct processing on both...

We review fundamental concepts of DSPG, including graph signals and graph filters, graph Fourier transform, graph frequency and spectrum ordering, and compare them with their counterparts from the classical signal processing theory.

We develop CompressGraph, an efficient rule-based graph analytics engine that leverages data redundancy in graphs to achieve both performance boost and space reduction for common graph applications. CompressGraph has three advantages over previous works.