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One-Max Constant-Probability Networks: Results and Future Work

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11085))

Abstract

In a number of our works we present and use the tree-like network models, so called one-max constant-probability models characterized by the following newly studied principles: (i) each new vertex may be connected to at most one existing vertex; (ii) any connection event is realized with the same probability p due to external factors; (iii) the probability \(\varPi \) 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. In this announcement we describe features and applications of these models and discuss possible ways of their generalization.

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Acknowledgments

The author thanks Ilya Levin, Eugene Levner and Vadim Talis for their contribution to the studies.

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Correspondence to Mark Korenblit .

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Korenblit, M. (2018). One-Max Constant-Probability Networks: Results and Future Work. In: Lotker, Z., Patt-Shamir, B. (eds) Structural Information and Communication Complexity. SIROCCO 2018. Lecture Notes in Computer Science(), vol 11085. Springer, Cham. https://doi.org/10.1007/978-3-030-01325-7_8

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  • DOI: https://doi.org/10.1007/978-3-030-01325-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01324-0

  • Online ISBN: 978-3-030-01325-7

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