Cargando…
A Review of Graph and Network Complexity from an Algorithmic Information Perspective
Information-theoretic-based measures have been useful in quantifying network complexity. Here we briefly survey and contrast (algorithmic) information-theoretic methods which have been used to characterize graphs and networks. We illustrate the strengths and limitations of Shannon’s entropy, lossles...
Autores principales: | Zenil, Hector, Kiani, Narsis A., Tegnér, Jesper |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513075/ https://www.ncbi.nlm.nih.gov/pubmed/33265640 http://dx.doi.org/10.3390/e20080551 |
Ejemplares similares
-
Symmetry and Correspondence of Algorithmic Complexity over Geometric, Spatial and Topological Representations †
por: Zenil, Hector, et al.
Publicado: (2018) -
The Thermodynamics of Network Coding, and an Algorithmic Refinement of the Principle of Maximum Entropy †
por: Zenil, Hector, et al.
Publicado: (2019) -
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
por: Zenil, Hector, et al.
Publicado: (2019) -
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity
por: Zenil, Hector, et al.
Publicado: (2018) -
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces
por: Hernández-Orozco, Santiago, et al.
Publicado: (2021)