Code underlying the publication: Online graph filter design over expanding graphs
doi:10.4121/aabf6ecd-ce11-4427-9fbc-9a769e16de49.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/aabf6ecd-ce11-4427-9fbc-9a769e16de49
doi: 10.4121/aabf6ecd-ce11-4427-9fbc-9a769e16de49
Datacite citation style:
Das, Bishwadeep (2024): Code underlying the publication: Online graph filter design over expanding graphs. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/aabf6ecd-ce11-4427-9fbc-9a769e16de49.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Research Objective is to design online algorithms for graph filter design over expanding graphs under conditions of known and unknown connectivity. The data-sets used in this paper are available online. Code for generating synthetic data is included. In the folder Recsys_new, the experimental setup and online algorithms for movie rating prediction for Movielens100k is provided. In the folder Stochastic_Synthetic_New, the experimental setup and online algorithms for signal interpolation for synthetic expanding graphs is included. In Stochastic_covid, the code for Covid case count prediction over a growing city network is provided.
history
- 2024-11-13 first online, published, posted
publisher
4TU.ResearchData
format
.mat files, .m files
associated peer-reviewed publication
Online Graph Filtering Over Expanding Graphs
code hosting project url
https://github.com/BD26992/Filtering-explanding-graph-signals
funding
- Graph Signal Processing in Action NWO
organizations
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems
DATA
To access the source code, use the following command:
git clone https://data.4tu.nl/v3/datasets/d97a97df-0f71-4581-b081-6262eaee2082.git "Filtering-explanding-graph-signals"
