Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/23207
Title: Weighted hypersoft configuration model
Authors: Voitalov, Ivan 
Van der Hoorn, Pim 
Kitsak, Maksim A. 
Papadopoulos, Fragkiskos 
Krioukov, Dmitri V. 
Major Field of Science: Natural Sciences
Field Category: Physical Sciences
Keywords: Physics;Physics and Society;Statistical Mechanics;Network;Topology;Weighted hypersoft configuration model;Joint distribution;Power-law degree distribution;Superlinear scaling between strengths and degrees;Sparsity
Issue Date: Dec-2020
Source: Physical Review Research, 2020, vol. 2, no. 4, articl. no. 043157
Volume: 2
Issue: 4
Journal: Physical Review Research 
Abstract: Maximum entropy null models of networks come in different flavors that depend on the type of constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given degree sequence is the configuration model per se. If the degree sequence is constrained only on average, the corresponding grand-canonical ensemble of random graphs with a given expected degree sequence is the soft configuration model. If the degree sequence is not fixed at all but randomly drawn from a fixed distribution, the corresponding hypercanonical ensemble of random graphs with a given degree distribution is the hypersoft configuration model, a more adequate description of dynamic real-world networks in which degree sequences are never fixed but degree distributions often stay stable. Here, we introduce the hypersoft configuration model of weighted networks. The main contribution is a particular version of the model with power-law degree and strength distributions, and superlinear scaling of strengths with degrees, mimicking the properties of some real-world networks. As a byproduct, we generalize the notions of sparse graphons and their entropy to weighted networks.
URI: https://hdl.handle.net/20.500.14279/23207
ISSN: 26431564
DOI: 10.1103/PhysRevResearch.2.043157
Rights: © The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license
Type: Article
Affiliation : Northeastern University 
Eindhoven University of Technology 
Delft University of Technology 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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