  The scientific literature is growing faster than ever. Finding an expert in a
particular scientific domain has never been as hard as today because of the
increasing amount of publications and because of the ever growing diversity of
expertise fields. To tackle this challenge, automatic expert finding algorithms
rely on the vast scientific heterogeneous network to match textual queries with
potential expert candidates. In this direction, document network embedding
methods seem to be an ideal choice for building representations of the
scientific literature. Citation and authorship links contain major
complementary information to the textual content of the publications. In this
paper, we propose a benchmark for expert finding in document networks by
leveraging data extracted from a scientific citation network and three
scientific question & answer websites. We compare the performances of several
algorithms on these different sources of data and further study the
applicability of embedding methods on an expert finding task.
