Code underlying the publication: Task-aware-connectivity-learning-for-incoming-nodes-over-growing-graphs
doi:10.4121/58f1571c-eb50-495c-8af5-16a978f3ee8c.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: 10.4121/58f1571c-eb50-495c-8af5-16a978f3ee8c
doi: 10.4121/58f1571c-eb50-495c-8af5-16a978f3ee8c
Datacite citation style:
Das, Bishwadeep (2024): Code underlying the publication: Task-aware-connectivity-learning-for-incoming-nodes-over-growing-graphs. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/58f1571c-eb50-495c-8af5-16a978f3ee8c.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Software
Code for the titled paper. Research objective is to find task-aware stochastic attachment models for cold start node inference. Data-sets used are available online.
Synthetic Data: Contains the code for the experimental setup shown in Section IV A of the paper
Movielens100k: Contains the code for the experimental setup shown in Section IV B of the paper (Data-Set used here to be found online)
Blog: Contains the code for the experimental setup shown in Section IV C of the paper (Data-Set used here to be found online)
history
- 2024-11-13 first online, published, posted
publisher
4TU.ResearchData
format
.mat files, .m files
associated peer-reviewed publication
Task-aware connectivity learning for incoming nodes over growing graphs
code hosting project url
https://github.com/BD26992/Task-aware-connectivity-learning-for-incoming-nodes-over-growing-graphs
funding
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/d2cc2e85-0c1d-41b1-9be4-2845bf879875.git "Task-aware-connectivity-learning-for-incoming-nodes-over-growing-graphs"
