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: 10.4121/0b40e329-7b33-4b8f-a289-ab6d1893b437

Touwen, Lourens; Bucur, Doina; van der Hofstad, Remco; Nelly Litvak (2023): Data underlying the publication: Learning the mechanisms of network growth. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/0b40e329-7b33-4b8f-a289-ab6d1893b437.v1

Dataset

• Artificial Intelligence and Image Processing
• Statistics
• Applied Mathematics
• Other Physical Sciences
• Information Systems
• Physical Sciences
• Mathematical Sciences
• Information and Computing Sciences

complex networks, machine learning, model selection for complex networks, networks with aging, networks with fitness, preferential attachment, synthetic networks, uniform attachment

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Lourens Touwen , Doina Bucur , Remco van der Hofstad , Nelly Litvak

This dataset accompanies the paper `Learning the mechanisms of network growth' by the same authors. The dataset contains 6733 networks of size 20,000 each generated in accordance to different combination of three mechanisms: fitness, aging and preferential attachment. The goal is to use machine learning to identify the combination of mechanisms that was used to create the network. The dataset includes static features from the literature and two version of our newly developed dynamic features. network growth

• 2023-09-19 first online, published, posted

4TU.ResearchData

.csv

TU Eindhoven, Department of Mathematics and Computer Science;
TU Eindhoven, Eindhoven Artificial Intelligence Systems Institute (EAISI)