Digital libraries provide different access paths, allowing users to explore their collections. For instance, paper recommendation suggests literature similar to some selected paper. Their implementation is often cost-intensive, especially if neural methods are applied. Additionally, it is hard for users to understand or guess why a recommendation should be relevant for them. That is why we tackled the problem from a different perspective. We propose XGPRec, a graph-based and thus explainable method which we integrate into our existing graph-based biomedical discovery system. Moreover, we show that XGPRec (1) can, in terms of computational costs, manage a real digital library collection with 37M documents from the biomedical domain, (2) performs well on established test collections and concept-centric information needs, and (3) generates explanations that proved to be beneficial in a preliminary user study. We share our code so that user libraries can build upon XGPRec.