Code underlying the publication: Tensor Graph Decomposition for Temporal Networks
doi:10.4121/3d04bb7a-d462-483b-8a26-984b0bf4dbfa.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: 10.4121/3d04bb7a-d462-483b-8a26-984b0bf4dbfa
doi: 10.4121/3d04bb7a-d462-483b-8a26-984b0bf4dbfa
Datacite citation style:
Das, Bishwadeep; Buciulea, Andrei (2024): Code underlying the publication: Tensor Graph Decomposition for Temporal Networks. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/3d04bb7a-d462-483b-8a26-984b0bf4dbfa.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Research Objective is to find out latent graphs which explain the evolution of dynamic graphs along with smooth graph signals. The data-sets are publicly available online, as indicated in the paper. The data is in .mat format. the file tgd_eff.m contains the main algorithm proposed.
history
- 2024-11-13 first online, published, posted
publisher
4TU.ResearchData
format
.m files, .mat files
associated peer-reviewed publication
Tensor Graph Decomposition for Temporal Networks
code hosting project url
https://github.com/BD26992/Dynamic-Graph-Decomposition
funding
- Graph Signal Processing in Action (grant code 19497) 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/abdcddca-1a2d-4dc5-b222-8c404ea18019.git "Dynamic-Graph-Decomposition"
