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
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"
