An information theory approach to network evolution models
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Publication:5103937
DOI10.1093/COMNET/CNAC020zbMATH Open1497.90058arXiv2201.08306OpenAlexW4281840095MaRDI QIDQ5103937
Amirmohammad Farzaneh, Justin P. Coon
Publication date: 9 September 2022
Published in: Journal of Complex Networks (Search for Journal in Brave)
Abstract: A novel Markovian network evolution model is introduced and analysed by means of information theory. It will be proved that the model, called Network Evolution Chain, is a stationary and ergodic stochastic process. Therefore, the Asymptotic Equipartition Property can be applied to it. The model's entropy rate and typical sequences are also explored. Extracting particular information from the network and methods to simulate network evolution in the continuous time domain are discussed. Additionally, the Erdos-Renyi Network Evolution Chain is introduced as a subset of our model with the additional property of its stationary distribution matching the Erdos-Renyi random graph model. The stationary distributions of nodes and graphs are calculated for this subset alongside its entropy rate. The simulation results at the end of the paper back up the proved theorems and calculated values.
Full work available at URL: https://arxiv.org/abs/2201.08306
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