We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce different simple views to large networks. We define a probabilistic similarity measure for nodes, and then apply clustering methods to nd groups of nodes. Finally, a representative is output from each cluster. We report on experiments with real biomedical data, using both the k-medoids and hierarchical clustering methods in the clustering step. The results suggest that the clustering based approaches are capable of finding a representative set of nodes.