A global cumulative centrality and structure entropy

In this paper, we propose a global cumulative centrality to identify influential nodes in complex networks. It measures a node's influence using both its local cumulative influence and that of its neighbors. And a new global cumulative structure entropy is proposed based on it. Experimental results on four constructed benchmark networks and six real-world networks show the superiority of our proposed method compared to some well-known classic centrality and structure entropy measurements. Then we use one of the real-world networks to give a brief introduction on how to analyze real complex networks with proposed metrics. This paper also shows the potential of global cumulative centrality in improving link prediction.