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Identifying influential nodes in complex networks has received increasingattention for its great theoretical and practical applications in many fields.Traditional methods, such as degree centrality, betweenness centrality,closeness centrality, and coreness centrality, have more or less disadvantagesin detecting influential nodes, which have been illustrated in relatedliteratures. Recently, the h-index, which is utilized to measure both theproductivity and citation impact of the publications of a scientist or scholar,has been introduced to the network world to evaluate a node's spreadingability. However, this method assigns too many nodes with the same value, whichleads to a resolution limit problem in distinguishing the real influence ofthese nodes. In this paper, we propose a local h-index centrality (LH-index)method for identifying and ranking influential nodes in networks. The LH-indexmethod simultaneously takes into account of h-index values of the node itselfand its neighbors, which is based on the idea that a node connects to moreinfluential nodes will also be influential. According to the simulation resultswith the stochastic Susceptible-Infected-Recovered (SIR) model in four realworld networks and several simulated networks, we demonstrate the effectivityof the LH-index method in identifying influential nodes in networks.

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