A number of methods of estimation are introduced, and are applied in the context of networks. Maximum likelihood estimation is applied to Bernoulli random graphs and to Erdős–Rényi mixture graphs. The EM algorithm, used later in fitting stochastic blockmodels, is also introduced. Both maximum likelihood and the (generalized) method of moments are used in the context of estimating the exponent of power law decay in degree distributions. Bayesian methods are presented, and the choice of prior discussed; they are applied to Erdős–Rényi mixture graphs and to their Poissonized variants. Further general methods introduced include Approximate Bayesian Computation, as well as Markov Chain Monte Carlo methods, for which both the Metropolis–Hastings algorithm and the Gibbs sampler are presented. Some specific models are given special attention. In exponential random graph models, MCMC methods offer an approach, though convergence to equilibrium can be very slow. The estimation of latent space models is discussed both from a frequentist and from a Bayesian point of view. Estimating the underlying dimension of a random geometric graph is also touched upon.