A new metric to quantify influence of nodes in social networks

Abstract

Reasonably ranking the influence of nodes in social networks is increasingly important not only for theoretical research but also for real applications. A great number of strategies to identify the influence of nodes have been proposed so far, such as semi-local centrality (SL), betweenness centrality and coreness centrality, etc. For the sake of ranking more effectively, a new method of identifying influential nodes is proposed in this paper, which takes into account a node’s influence on its neighbors and the node’s position in the network. The influence on neighbors involves two aspects. One is the influence of the target node on its direct neighbors (h-index), the other is the influence on farther neighbors (semi-local centrality). The location of the node in the network is reflected by the improved k-core score, a modified version of k-core index to make it more applicable to practice. Combining both local and global information of node together makes the proposed method a reasonable and effective strategy to identify the influential nodes. The simulation results compared to other well-known methods on six real-world networks demonstrate the effectiveness of the presented method.