Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

The effective representation, processing, analysis, and visualization of large-scale structured
data, especially those related to complex domains, such as networks and graphs, are one of …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a
variety of graph-related applications. However, the performance of GNNs can be …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

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 …

Given the prevalence of large-scale graphs in real-world applications, the storage and time
for training neural models have raised increasing concerns. To alleviate the concerns, we …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

Identifying graph topologies as well as processes evolving over graphs emerge in various
applications involving gene-regulatory, brain, power, and social networks, to name a few …