Modeling data as a graph of objects is increasingly popular, as we move away from the relational DB model and try to introduce explicit semantics in IR. Conceptually, one of the main challenges in this context is how to"intelligently"traverse the graph and exploit the associations between the data objects. Two highly used methods in retrieving information on structured data are: Markov chain random walks, as is the basic method for page rank, and spreading activation, which originates from the artificial intelligence area. In this paper, we compare these two methods from a mathematical point of view. Random walks have been preferred in information retrieval, while spreading activation has been proposed before, but not really adopted. In this study we find that they are very similar fundamentally under certain conditions. However, spreading activation has much more flexibility and customization options, while random walks holds concise mathematics foundation.