A New Method for Ranking the Most Influential Node in Complex Networks

Abstract

Currently, understanding the topology structure and function of complex networks has become a hot research topic, and finding the most influential node in a complex network has great significance in marketing, public opinion analysis, disease control and so on. We often use the degree centrality and some other centralities to measure the impact of the node, but they only reflect partial nature of the network. In order to describe the key nodes of the network more accurately, in this paper, we present a new method of ranking the most influential node in a complex network, which not only takes the degree centrality into consideration but also takes the position of the nodes in the network and the nodes' important neighbors into account. Then we use the Independent Cascade Model for propagation simulation. Experiments show that the method we proposed could identify the most influential nodes more effectively. Compared with the traditional method it has better dissemination of results and lower time complexity.