We propose a sampling theory for signals that are supported on either directed or undirected
graphs. The theory follows the same paradigm as classical sampling theory. We show that …

Signals and datasets that arise in physical and engineering applications, as well as social,
genetics, biomolecular, and many other domains, are becoming increasingly larger and …

Signal processing on graph is attracting more and more attentions. For a graph signal in the
low-frequency subspace, the missing data associated with unsampled vertices can be …

The rapid development of signal processing on graphs provides a new perspective for
processing large-scale data associated with irregular domains. In many practical …

Periodic impulses are vital indicators of rolling bearing faults. The extraction of impulse
components from rolling bearing vibration signals is of great importance for fault diagnosis …

At the dawn of the Internet era graph analytics play an important role in high-and low-level
network policymaking across a wide array of fields so diverse as transportation network …

This book provides a comprehensive introduction to the use of graph analysis in the study of
social media and digital media. It covers the following topics: graphs in social media, graph …

Multiresolution analysis is important for understanding graph signals, which represent graph-
structured data. Wavelet filterbanks permit multiscale analysis and processing of graph …

Graph semi-supervised learning (GSSL) is a technique that uses a combination of labeled
and unlabeled nodes on a graph to determine a classifier for new, incoming data. This …

Graph-structured data appears in many modern applications like social networks, sensor
networks, transportation networks and computer graphics. These applications are defined by …