Link prediction based on node centrality

Identifying vital nodes is crucial in researching the structures and evolution of complex networks. Most existing link prediction methods utilize node degree as the measure of node importance. But degree is less accurate in evaluating the importance of nodes since it exploit very limited information. Therefore, we introduce node centrality to identify vital nodes. This paper proposes a link prediction method based on node centrality to improve accuracy, which can distinguish the endpoint influence and path connectivity. We reveal that closeness centrality describe the endpoint influence better than degree and betweenness centrality. and betweenness centrality quantifies the path connectivity best.