Year: 2021
Type: article
Abstract: Recent advances in Machine Learning, an area that leverages data to identify patterns, and in Complex Networks, an area which leverages connections between entities to identify complex phenomena and can be considered as an extension to graph theory, are changing society.Both of those areas can be related to the task of 'learning' from data (Silva & Zhao, 2016).There seems, however, to be a gap between those two research areas.It has already been shown that one can leverage both, using complex networks to improve machine learning methods, and using machine learning to exploit the information on complex networks to achieve better results.However, little to no implementation of methods that can be used on both areas has been open-sourced.And for those who did, it was not done in any unified way.sknet exists as a library to solve this gap.
DOI: 10.21105/joss.03864
Author: Tiago Toledo
Institution: Universidade de São Paulo
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4200310606