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 …

Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

A number of problems can be formulated as prediction on graph-structured data. In this
work, we generalize the convolution operator from regular grids to arbitrary graphs while …

We present graph wavelet neural network (GWNN), a novel graph convolutional neural
network (CNN), leveraging graph wavelet transform to address the shortcomings of previous …

Incomplete multi-view clustering aims to exploit the information of multiple incomplete views
to partition data into their clusters. Existing methods only utilize the pair-wise sample …

Abstract Moving Object Segmentation (MOS) is an important topic in computer vision. MOS
becomes a challenging problem in the presence of dynamic background and moving …

We propose a framework to learn the graph structure underlying a set of smooth signals.
Given X∈\mathbbR^ m\times n whose rows reside on the vertices of an unknown graph, we …

Algebraic graph theory is a cornerstone in the study of electrical networks ranging from
miniature integrated circuits to continental-scale power systems. Conversely, many …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …