Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. La Rochelle, France. 

Further Improvements

If you would like to list your publication related to this topic on this website, please send me your publication in .pdf and I will add the reference.

Fair Use Policy

As this website gives many information that come from my research, please cite my following papers:

J. Castro-Correa, J. Giraldo, A. Mondal, M. Badiey, T. Bouwmans, F. Malliaros, "Time-Varying Signals Recovery Via Graph Neural Networks", IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, June 2023. 

W. Prummel, J. Giraldo, A. Zakharova, T. Bouwmans, "Inductive Graph Neural Networks for Moving Object Segmentation", IEEE International Conference on Image Processing, ICIP 2023, Kuala Lumpur, Malaysia, October 2023. 

Objective

The aim of this web site is to provide resources such as references (441 papers), datasets (datasets), codes (codes) and links to demonstration websites (websites)  for the research on graph signal processing, graph neural networks and graph spectral clustering by grouping all related researches and particularly recent advances in this field. For this, it  is organized in the following sections:

A) Graph Signal Processing / Hypergraph Signal Processing (141 papers)

• Graph Signal Processing (140 papers): Sampling Signal Techniques (29 papers), Recovery Techniques (28 papers), Time Varying Graphs (49 papers), Adaptive Graph Signal Processing (1 paper), Graph Fourier Transform (19 papers), Distributed Graph Signal Processing (4 papers), Parallel Graph Signal Processing (1 paper), Generalized Graph Signal Processing (3 papers),  Signed Graph Signal Processing (4 papers), Graph DCT (1 paper), 

• Hypergraph Graph Signal Processing (1 paper): Scalable (1 paper)

B) Graph Neural Networks / Hypergraph Neural Networks (182 papers)

• Graph Neural Networks (160 papers): Theory (32 papers), Challenges (39 papers), Robustness (23 papers),  Graph Attention Networks (1 paper), Applications (2 papers), Vision GNN (5 papers),  Accelerated Implementations (49 papers), Distributed Graph Neural Networks (3 papers), Heterogeneous Graph Neural Networks (1 paper), Inductive Graph Neural Networks (1 paper), Continuous Graph Neural Networks (4 papers),  Graph Neural Diffusion (1 paper)

• Probabilistic Graph Neural Networks (1 paper)

• Fuzzy Graph Neural Networks (6 papers)

• Hypergraph Neural Networks (13 papers) : Theory (6 papers), Applications (7 papers)

• Probabilistic Hypergraph Neural Networks (2 papers)

• Fuzzy Hypergraph Neural Networks (3 papers) : Theory (3 papers)

C) Graph Spectral Clustering (1 paper)

Application (1 paper)

D) Graph Learning (1 paper)

E) Graph Similarity (2 papers)

G) Applications [Graph Signal Processing] (95 papers)

• Applications in Signal Processing (11 papers)

Acoustics (7 papers) , Signal (2 papers), Radar (1 paper), Direction-of-Arrival Estimation (1)

• Applications in Computer Science (7 papers)

Recommendation System (6 papers), Computers Networks (1 paper)

• Applications in Computer Vision (15 papers)

Image Processing (3 papers), Video Processing (7 papers), 3D Computer Vision (5 papers)

• Applications in Physics  (9 papers)

• Applications in Chemistry (13 papers)

• Applications in Industry (2 papers)

• Applications in Electricity (3 papers)

•  Applications in Electronics (3 papers)

• Applications in Biology (1 paper)

• Applications - Others (26  papers)

Feature Extraction (1 paper), Time Series Forecasting (1 paper), Traffic Forecasting (12 papers), Transportation Networks (1 paper), Ecology (17 papers)

H) Surveys (14 papers)