RETRACTED ARTICLE: Identifying vital nodes in hypernetwork based on local centrality

Identifying vital nodes in hypernetworks is of great significance for understanding the connectivity property and dynamic characteristic of the hypernetwork. A number of methods have been proposed to identify vital nodes of hypernetworks, ranging from centralities of nodes to diffusion-based processes, but most of them ignore the impacts of neighbors. Many researchers use degree, hyper-degree or the clustering coefficient to identify vital nodes. However, the degree can only take into account the neighbor size, the hyper-degree can only consider the incidence hyperedge size, regardless of the clustering property of the neighbors. The clustering coefficient could only reflect the density of connections among the neighbors and neglect the activity of the target node. In this paper, we present a novel local centrality to identify vital nodes by combining the influence of the node itself and neighbor as well as clustering coefficient information. To evaluate the performance of the proposed method, the robustness results measured by the hypernetwork efficiency through removing the vital nodes for protein complex hypernetwork show that the new method can more effective in identify vital nodes.