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With the increasing availability of online scholarly databases, publicationrecords can be easily extracted and analysed. Researchers can promptly keepabreast of others' scientific production and, in principle, can select newcollaborators and build new research teams. A critical factor one shouldconsider when contemplating new potential collaborations is the possibility ofunambiguously defining the expertise of other researchers. While someorganisations have established database systems to enable their members tomanually produce a profile, maintaining such systems is time-consuming andcostly. Therefore, there has been a growing interest in retrieving expertisethrough automated approaches. Indeed, the identification of researchers'expertise is of great value in many applications, such as identifying qualifiedexperts to supervise new researchers, assigning manuscripts to reviewers, andforming a qualified team. Here, we propose a network-based approach to theconstruction of authors' expertise profiles. Using the MEDLINE corpus as anexample, we show that our method can be applied to a number of widely used datasets and outperforms other methods traditionally used for expertiseidentification.

A network approach to expertise retrieval based on path similarity and credit allocation