Identifying spreading influential nodes in networks with community structures has attracted increasing attention recently. In this paper, by taking into the local and global information, we present an improved method to measure node spreading influence, namely the ISGC method. This method present an improved isolating centrality to measure target nodes’ local influence, then combine it with the global influence measurement namely k-shell decomposition method to identify the influential nodes. Experimental results for six empirical networks and four LFR synthetic networks show that, comparing with the tradition ILGC method, the kendall’s τ could be enhanced by 2.13%. Furthermore, the experimental results show that the ISGC method is more effective for networks with community structures.
Guo et al. (Thu,) studied this question.