.png){kind=icon}
.png){kind=icon}
Return
Tame sparse exponential random graphs
.png){kind=icon}
PRE
AI
DOI：10.3150/25-bej1926.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we obtain a precise estimate of the probability that the sparse binomial random graph contains a large number of vertices in a triangle. We compute the logarithm of this probability up to second order, which enables us to propose an exponential random graph model based on the number of vertices in a triangle. Specifically, by tuning a single parameter, we can with high probability induce any given fraction of vertices in a triangle. Moreover, in the proposed exponential random graph model we derive a large deviation principle for the number of edges. As a byproduct, we propose a consistent estimator of the tuning parameter.
Keywords:
Consistent estimation
exponential random graph
nonlinear large deviations
random graphs
Journal
B
IF:
1.7
Papers:
106
Citations:
0