Learning reputation in an authorship network

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. To demonstrate the efficacy of the approach we report on a set of computational simulations using the large Arnetminer dataset.