Data analytics on graphs part III: Machine learning on graphs, from graph topology to applications
Modern data analytics applications on graphs often operate on domains where graph
topology is not known a priori, and hence its determination becomes part of the problem …
topology is not known a priori, and hence its determination becomes part of the problem …
Introduction to graph signal processing
Graph signal processing deals with signals whose domain, defined by a graph, is irregular.
An overview of basic graph forms and definitions is presented first. Spectral analysis of …
An overview of basic graph forms and definitions is presented first. Spectral analysis of …
Cengcn: Centralized convolutional networks with vertex imbalance for scale-free graphs
Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide
variety of areas, attracting considerable attention. The core step of GCNs is the information …
variety of areas, attracting considerable attention. The core step of GCNs is the information …
Modeling and recovery of graph signals and difference-based signals
A Kroizer, YC Eldar… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
In this paper, we consider the problem of representing and recovering graph signals with a
nonlinear measurement model. We propose a two-stage graph signal processing (GSP) …
nonlinear measurement model. We propose a two-stage graph signal processing (GSP) …
Graph signal processing--Part II: Processing and analyzing signals on graphs
The focus of Part I of this monograph has been on both the fundamental properties, graph
topologies, and spectral representations of graphs. Part II embarks on these concepts to …
topologies, and spectral representations of graphs. Part II embarks on these concepts to …
Nonlinear polynomial graph filter for signal processing with irregular structures
Processing of signals with irregular structures is a fundamental challenge to the
conventional signal processing due to the complex structures. Since most of the …
conventional signal processing due to the complex structures. Since most of the …
Averting cascading failures in networked infrastructures: Poset-constrained graph algorithms
Cascading failures in critical networked infrastructures that result even from a single source
of failure often lead to rapidly widespread outages as witnessed in the 2013 Northeast …
of failure often lead to rapidly widespread outages as witnessed in the 2013 Northeast …
Z-Laplacian Matrix Factorization: Network Embedding With Interpretable Graph Signals
Network embedding aims to represent nodes with low dimensional vectors while preserving
structural information. It has been recently shown that many popular network embedding …
structural information. It has been recently shown that many popular network embedding …
A filtering framework for time-varying graph signals
Time-varying graph signal processing generalizes scalar graph signals to multivariate time-
series data with an underlying graph structure. Important applications include network …
series data with an underlying graph structure. Important applications include network …
Black-Box Attacks On Graph Neural Networks via White-Box Methods With Performance Guarantees
Graph adversarial attacks can be classified as either white-box or black-box attacks. White-
box attackers typically exhibit better performance because they can exploit the known …
box attackers typically exhibit better performance because they can exploit the known …