In this repository, I intend to provide python implementations of graph learning algorithms that I have used successfully to construct graphs from data for the tasks described in the following papers, which I have (co)authored:
- Block-based spectral image reconstruction using smoothness on graphs (2022).
- Graph learning based on signal smoothness representation for homogeneous and heterogenous change detection (2022).
Particularly, this first version of the repo includes the graph learning model developed by Vassilis Kalofolias et al. in the papers:
** How to learn a graph from smooth signals (2016), and large scale graph learning from smooth signals (2017).**
I will however update the repo with new models not yet implemented in python and some of my own models.