  In this paper, we study the impact of combining profile and network data in a
de-duplication setting. We also assess the influence of a range of prior
distributions on the linkage structure. Furthermore, we explore stochastic
gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain
samples from the posterior distribution for network parameters. Our methodology
is evaluated using the RLdata500 data, which is a popular dataset in the record
linkage literature.
