Prominence and dominance in networks

Abstract

Topographic prominence and dominance were recently developed to quantify the relative importance of mountain peaks. Instead of simply using the height to characterize a mountain, they provide a more meaningful description based on vertical and horizontal distances in the neighborhood. In this paper, we propose structural prominence and dominance for networks, an adaptation of the topographic measures, for the detection of nodes with strong local importance. We create a network “landscape” which is generated by a node’s height and distance to other nodes in the network. We ground our proposed measures on the task of predicting award winners with high and sustainable impact in a co-authorship network. Our experiments show that our measures provide information about a graph, that is not provided by other graph measures.