scholarly journals Exponential random graphs behave like mixtures of stochastic block models

2018 ◽  
Vol 28 (6) ◽  
pp. 3698-3735 ◽  
Author(s):  
Ronen Eldan ◽  
Renan Gross
2013 ◽  
Vol 23 (6) ◽  
pp. 2458-2471 ◽  
Author(s):  
Charles Radin ◽  
Mei Yin

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
A Gorsky ◽  
O Valba

Abstract In this article, we show numerically the strong finite-size effects in exponential random graphs. Particularly, for the two-star model above the critical value of the chemical potential for triplets a ground state is a star-like graph with the finite set of hubs at network density $p<0.5$ or as the single cluster at $p>0.5$. We find that there exists the critical value of number of nodes $N^{*}(p)$ when the ground state undergoes clear-cut crossover. At $N>N^{*}(p),$ the network flows via a cluster evaporation to the state involving the small star in the Erdős–Rényi environment. The similar evaporation of the cluster takes place at $N>N^{*}(p)$ in the Strauss model. We suggest that the entropic trap mechanism is relevant for microscopic mechanism behind the crossover regime.


2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Giona Casiraghi

AbstractWe provide a novel family of generative block-models for random graphs that naturally incorporates degree distributions: the block-constrained configuration model. Block-constrained configuration models build on the generalized hypergeometric ensemble of random graphs and extend the well-known configuration model by enforcing block-constraints on the edge-generating process. The resulting models are practical to fit even to large networks. These models provide a new, flexible tool for the study of community structure and for network science in general, where modeling networks with heterogeneous degree distributions is of central importance.


2011 ◽  
Vol 21 (6) ◽  
pp. 2146-2170 ◽  
Author(s):  
Shankar Bhamidi ◽  
Guy Bresler ◽  
Allan Sly

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