Node Importance Identification for Temporal Network Based on Ranking Aggregation

At present, the research on the importance of temporal network nodes is mainly carried out from the aspects of timing path, connectivity, network efficiency and so on. In this paper, we consider the inter-layer connection and inter-layer coupling, introduce the ranking aggregation theory based on scoring matrix, and propose a method based on ranking aggregation to identify the importance of nodes in temporal networks. Manufacturing and Enrons datasets show that the average increase of Spearman correlation coefficient based on ranking aggregation is 2.41% and 18.63%, which shows the applicability and effectiveness of this method in the measurement of node importance in temporal network.