Real world social networks typically consist of actors (individuals) that are linked to other actors or different types of objects via links of multiple types. Different types of relationships induce different views of the underlying social network. We consider the problem of labeling actors in such multi-view networks based on the connections among them. Given a social network in which only a subset of the actors are labeled, our goal is to predict the labels of the rest of the actors. We introduce a new random walk kernel, namely the Inter-Graph Random Walk Kernel (IRWK), for labeling actors in multi-view social networks. IRWK combines information from within each of the views as well as the links across different views. The results of our experiments on two real-world multi-view social networks show that: (i) IRWK classifiers outperform or are competitive with several state-of-the-art methods for labeling actors in a social network; (ii) IRWKs are robust with respect to different choices of user-specified parameters; and (iii) IRWK kernel computation converges very fast within a few iterations.