  We present a comprehensive extension of the latent position network model
known as the random dot product graph to accommodate multiple graphs -- both
undirected and directed -- which share a common subset of nodes, and propose a
method for jointly embedding the associated adjacency matrices, or submatrices
thereof, into a suitable latent space. Theoretical results concerning the
asymptotic behaviour of the node representations thus obtained are established,
showing that after the application of a linear transformation these converge
uniformly in the Euclidean norm to the latent positions with Gaussian error.
Within this framework, we present a generalisation of the stochastic block
model to a number of different multiple graph settings, and demonstrate the
effectiveness of our joint embedding method through several statistical
inference tasks in which we achieve comparable or better results than rival
spectral methods. Empirical improvements in link prediction over single graph
embeddings are exhibited in a cyber-security example.
