K-hop centrality metric for identifying influential spreaders in dynamic large-scale social networks

Identifying the most influential spreaders in social networks has many practical applications. The existing methods for the purpose are either too time-consuming for dynamic large-scale networks, such as betweenness centrality, closeness centrality, eigenvector centrality and Katz centrality, or do not consider the network topology, such as degree centrality. To design an effective method to identify the most influential nodes in a network, we propose a novel metric, k-hop centrality which is a generalization of degree centrality. The k-hop index is the summation of the number n(i) of nodes within k-hop distance from the node in question, attenuated by 1/αⁱ, for 1 ≤ i ≤ k (α is the average degree of nodes in the network). It is calculated in a localized manner and is complexity-scalable by adjusting the value of k, thus suitable for dynamically changing, large social networks. We adopt the Susceptible Infected Recovered (SIR) model to evaluate the performance of k-hop centrality over four real datasets of complex networks, and experimental results show that our method outperforms state-of-the-art methods in this field in terms of both infection ratios (spreading influence) and computational complexity. Our work sheds some light on designing efficient spreading strategies for complex networks.