Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document's association with venues and citation links with other documents. We propose graph-based models that accommodate multiple sources of evidence in a PageRank-like algorithm for ranking experts. Our studies on two publicly-available datasets indicate that our model despite being general enough to be directly useful for ranking other types of objects performs on par with probabilistic models commonly used for expert ranking.