Abstract
This paper presents a number of the tree-like networks that grow according to the following newly studied principles: i) each new vertex can be connected to at most one existing vertex; ii) any connection event is realized with the same probability p; iii) the probability Π that a new vertex will be connected to vertex i depends not directly on its degree d i but on the place of d i in the sorted list of vertex degrees. The paper proposes a number of models for such networks, which are called one-max constant-probability models. In the frame of these models, structure and behavior of the corresponding tree-like networks are studied both analytically, and by using computer simulations.
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Korenblit, M., Talis, V., Levin, I. (2014). One-Max Constant-Probability Models for Complex Networks. In: Contucci, P., Menezes, R., Omicini, A., Poncela-Casasnovas, J. (eds) Complex Networks V. Studies in Computational Intelligence, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-05401-8_17
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DOI: https://doi.org/10.1007/978-3-319-05401-8_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05400-1
Online ISBN: 978-3-319-05401-8
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