scholarly journals Three-species monomer-monomer model: A mean-field analysis and Monte Carlo study

1997 ◽  
Vol 55 (5) ◽  
pp. 5225-5233 ◽  
Author(s):  
Kevin E. Bassler ◽  
Dana A. Browne
1997 ◽  
Vol 56 (4) ◽  
pp. 3953-3958 ◽  
Author(s):  
K. S. Brown ◽  
K. E. Bassler ◽  
D. A. Browne

2022 ◽  
Vol 2022 (1) ◽  
pp. 013402
Author(s):  
Xiang Li ◽  
Mauro Mobilia ◽  
Alastair M Rucklidge ◽  
R K P Zia

Abstract We investigate the long-time properties of a dynamic, out-of-equilibrium network of individuals holding one of two opinions in a population consisting of two communities of different sizes. Here, while the agents’ opinions are fixed, they have a preferred degree which leads them to endlessly create and delete links. Our evolving network is shaped by homophily/heterophily, a form of social interaction by which individuals tend to establish links with others having similar/dissimilar opinions. Using Monte Carlo simulations and a detailed mean-field analysis, we investigate how the sizes of the communities and the degree of homophily/heterophily affect the network structure. In particular, we show that when the network is subject to enough heterophily, an ‘overwhelming transition’ occurs: individuals of the smaller community are overwhelmed by links from the larger group, and their mean degree greatly exceeds the preferred degree. This and related phenomena are characterized by the network’s total and joint degree distributions, as well as the fraction of links across both communities and that of agents having fewer edges than the preferred degree. We use our mean-field theory to discuss the network’s polarization when the group sizes and level of homophily vary.


2008 ◽  
Vol 113 (1) ◽  
pp. 461-464 ◽  
Author(s):  
J. Dely ◽  
A. Bobák ◽  
D. Horváth

1998 ◽  
Vol 09 (07) ◽  
pp. 1107-1119 ◽  
Author(s):  
Eric Bonabeau ◽  
Florian Hénaux

A Monte Carlo algorithm for partitioning graphs is presented. The algorithm is based on the self-organizing map, an unsupervised, competitive neural network. A mean-field analysis suggests that the complexity of the algorithm is at most is on the order of O(n3/|E|), where n is the number of vertices of the graph, and |E| the number of edges. This prediction is tested on a class of random graphs. Scaling laws that deviate from the mean-field prediction are obtained. Although the origin of these scaling laws is unclear, their consequences are discussed.


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