Ranking influential spreaders based on both node k-shell and structural hole

The ranking of individual spreaders aims to measure the influential capability of individual nodes and is important to control information spreading in a network. However, many ranking methods are either degree-based, k-shell-related or a combination of the two, which are not necessarily related to influential capability. Inspired by the strengths of the k-shell decomposition method, this work improves it on the basis of structural holes (SH) and proposes a novel ranking method, SHKS. Different from the efforts that aim only to improve the k-shell decomposition method, this work considers the k-shell and SH-based centrality of a node as well as its neighbors and second-order neighbors. Based on the flexible combination of k-shell and SH, SHKS can identify not only the core nodes with large k-shell indices but also the nodes that have small k-shell indices but play an important role in bridging different parts of a network. Experimental results show that SHKS presents better performance than baseline methods in terms of the Kendall τ correlation results, and the average improvements range from 1.3% to 121.1%. SHKS also has the best monotonicity, and its average monotonicity value on experimental networks is close to 0.99. Moreover, SHKS has good performance in identifying the most influential top-k nodes compared with baseline methods.