This article provides an introductory summary to the formulation and application of exponential random graph models for social networks. The possible ties among nodes of a network are regarded as random variables, and assumptions pdf of sum of 2 exponential random variables common parameter dependencies among these random tie variables determine the general form of the exponential random graph model for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov random graph models. The incorporation of actor attributes in social selection models is also reviewed.

Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. Check if you have access through your login credentials or your institution. Unsourced material may be challenged and removed. In each of these three forms, both parameters are positive real numbers. Both parametrizations are common because either can be more convenient depending on the situation.

Unlike the mode and the mean which have readily calculable formulas based on the parameters, the median does not have an easy closed form equation. 1 which are set to 1, 2, 3, 4, 5 and 6. The typical asymmetry for the KL divergence is clearly visible. 1 as we can later convert to any value of β with simple division.