In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation network, the MRTM models the citation link between each pair of documents as a binary variable conditioned on their topic distributions. In the coauthor network, the MRTM models the coauthor link between each pair of authors as a binary variable conditioned on their expertise distributions. The topic discovery is collectively regularized by multiple relations in both citation and coauthor networks. This model can summarize topics from the document network, predict citation links between documents and coauthor links between authors. Efficient inference and learning algorithms are derived based on Gibbs sampling. Experiments demonstrate that the MRTM significantly outperforms other state-of-the-art single-relational link modeling methods for large scientific document networks.