Ranking Nodes in Temporal Networks: Eigen Value and Node Degree Growth based

Abstract

Compared with the traditional static network, the temporal networks can describe the events in the network in more detail and comprehensively. The static network only considers the connection between nodes and ignores the process of network development, while the temporal network lacks the process of finding important nodes in the network. In real life, many networks can be described as temporal networks, such as communication networks, infectious disease transmission networks, and the activation of neurons inside the brain. An important node of a temporal network is that it can affect the structure and function of the network to a greater extent than other nodes of the network. This paper conducts research and analysis on the ranking of important nodes in temporal networks. This article summarizes several prioritization methods of node importance in temporal networks and compares them with the importance ranking of nodes in static networks. Finally, this paper proposes two methods for ranking the importance of nodes in the time network: eigenvalue centrality and growth-based centrality. The experiment is conducted on two real datasets, one is social networking college message and another one is based on real human contact network.