Graph learning
==============================
Collection of models for learning networks from signals.
Clustering methods follow the [sklearn](https://scikit-learn.org/stable/) API.
## Installation
Clone the git repository and install with pip:
```
git clone https://github.com/LTS4/graph-learning.git
cd graph-learning
pip install .
```
## References
### Base Models
#### Smooth learning (LogModel)
> V. Kalofolias, “How to Learn a Graph from Smooth Signals,” in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, May 2016, pp. 920–929. https://doi.org/10.48550/arXiv.1601.02513.
> V. Kalofolias and N. Perraudin, “Large Scale Graph Learning From Smooth Signals,” presented at the International Conference on Learning Representations, Sep. 2018. Available: https://openreview.net/forum?id=ryGkSo0qYm
Part of the code is ported to Python from the Matlab implementation from https://github.com/epfl-lts2/gspbox, published under GNU General Public License v3.0.
#### LGRMF
> H. E. Egilmez, E. Pavez, and A. Ortega, “Graph learning with Laplacian constraints: Modeling attractive Gaussian Markov random fields,” in 2016 50th Asilomar Conference on Signals, Systems and Computers, Nov. 2016, pp. 1470–1474. https://doi.org/10.1109/ACSSC.2016.7869621.
### Clustering models
#### GLMM
> H. P. Maretic and P. Frossard, “Graph Laplacian Mixture Model,” IEEE Transactions on Signal and Information Processing over Networks, vol. 6, pp. 261–270, 2020, https://doi.org/10.1109/TSIPN.2020.2983139.
#### k-Graphs
> H. Araghi, M. Sabbaqi, and M. Babaie–Zadeh, “$K$-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph Learning,” IEEE Signal Processing Letters, vol. 26, no. 10, pp. 1486–1490, Oct. 2019, https://doi.org/10.1109/LSP.2019.2936665.
### Temporal graph learning
#### TGFA
> K. Yamada, Y. Tanaka, and A. Ortega, “Time-Varying Graph Learning with Constraints on Graph Temporal Variation,” Jan. 10, 2020, https://doi.org/10.48550/arXiv.2001.03346.
#### Temporal Multiresolution Graph Learning (GraphDictHier)
> K. Yamada and Y. Tanaka, “Temporal Multiresolution Graph Learning,” IEEE Access, vol. 9, pp. 143734–143745, 2021, https://doi.org/10.1109/ACCESS.2021.3120994.
### Dictionary Models
#### Parametric Dictionary Learning (GraphDictSpectral)
> D. Thanou, D. I. Shuman, and P. Frossard, “Parametric dictionary learning for graph signals,” in 2013 IEEE Global Conference on Signal and Information Processing, Dec. 2013, pp. 487–490. https://doi.org/10.1109/GlobalSIP.2013.6736921.
#### Graph Dictionary Signal Model (GraphDictLog, GraphDictBase)
> W. Cappelletti and P. Frossard, “Graph-Dictionary Signal Model for Sparse Representations of Multivariate Data,” Nov. 08, 2024, [arXiv:2411.05729](https://arXiv.org/abs/2411.05729)
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