Information-rich relational graphs have shown great potential in designing effective representations of code for program-understanding tasks. However, the wealth of structural and semantic information in such graphs can overwhelm models, because of their limited input size. A promising approach for overcoming this challenge is to gather presumed-relevant but smaller context from a larger graph, and random walks over graphs was one of the first such approaches discovered. We propose a deep-learning approach that improves upon random walks by learning task-specific walk policies that guide the traversal of the graph towards the most relevant context. In the setting of relational graphs representing programs and their semantic properties, we observe that models that employ learned policies for guiding walks are 6-36% points more accurate than models that employ uniform random walks, and 0.2-3.5% points more accurate than models that employ expert knowledge for guiding the walks.