Graph signal processing for machine learning: A review and new perspectives
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 …
data, especially those related to complex domains, such as networks and graphs, are one of …
Graph representation learning: a survey
Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …
since most data in real-world applications come in the form of graphs. High-dimensional …
Connecting the dots: Identifying network structure via graph signal processing
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 …
processing (GSP) efforts to date assume that the underlying network is known and then …
Learning graphs from data: A signal representation perspective
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 …
representation, processing, analysis, and visualization of structured data. When a natural …
Network topology inference from spectral templates
We address the problem of identifying the structure of an undirected graph from the
observation of signals defined on its nodes. Fundamentally, the unknown graph encodes …
observation of signals defined on its nodes. Fundamentally, the unknown graph encodes …
Characterization and inference of graph diffusion processes from observations of stationary signals
Many tools from the field of graph signal processing exploit knowledge of the underlying
graph's structure (eg, as encoded in the Laplacian matrix) to process signals on the graph …
graph's structure (eg, as encoded in the Laplacian matrix) to process signals on the graph …
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 …
Learning laplacians in chebyshev graph convolutional networks
H Sahbi - Proceedings of the IEEE/CVF International …, 2021 - openaccess.thecvf.com
Spectral graph convolutional networks (GCNs) are particular deep models which aim at
extending neural networks to arbitrary irregular domains. The principle of these networks …
extending neural networks to arbitrary irregular domains. The principle of these networks …
Graph structure learning with interpretable Bayesian neural networks
M Wasserman, G Mateos - arXiv preprint arXiv:2406.14786, 2024 - arxiv.org
Graphs serve as generic tools to encode the underlying relational structure of data. Often
this graph is not given, and so the task of inferring it from nodal observations becomes …
this graph is not given, and so the task of inferring it from nodal observations becomes …
Kernel regression over graphs using random Fourier features
VRM Elias, VC Gogineni, WA Martins… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
This paper proposes efficient batch-based and online strategies for kernel regression over
graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal …
graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal …