Leveraging local h-index to identify and rank influential spreaders in networks

Identifying influential nodes in complex networks has received increasing attention for its great theoretical and practical applications in many fields. Some classical methods, such as degree centrality, betweenness centrality, closeness centrality, and coreness centrality, were reported to have some limitations in detecting influential nodes. Recently, the famous h-index was introduced to the network world to evaluate the spreading ability of the nodes. However, this method always assigns too many nodes with the same value, which leads to a resolution limit problem in distinguishing the real influences of these nodes. In this paper, we propose a local h-index centrality (LH-index) method to identify and rank influential nodes in networks. The LH-index method simultaneously takes into account of h-index values of the node itself and its neighbors, which is based on the idea that a node connecting to more influential nodes will also be influential. Experimental analysis on stochastic Susceptible-Infected-Recovered (SIR) model and several networks demonstrates the effectivity of the LH-index method in identifying influential nodes in networks.