Finding representative nodes in probabilistic graphs
We introduce the problem of identifying representative nodes in probabilistic graphs, motivated by the need to produce dierent simple views to large networks. We dene 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 nding a representative set of nodes.