Abstract: The gravity model can effectively fuse multiple information of nodes, which make up for the problem of incomplete node information considered by traditional node importance evaluation methods. However, the existing gravity model related methods consider a single factor when defining node mass, and ignore the important role of neighbor topology in measuring node importance. To solve the above problems, a method for evaluating the importance of nodes in complex networks based on the gravity model and node hierarchy distribution is proposed, named HDG. Firstly, the nodes’ neighborhood and position information are fused to represent the mass of objects in the gravity model. Secondly, the influence of the topological similarity between nodes and neighbors on the information transmission between nodes is considered. Finally, the importance of nodes is measured by the interaction between nodes and neighbor nodes within a given scope. The simulation on six real network datasets shows that the proposed method performs better than other gravity model-related ones in both monotonicity and accuracy.

Key words: complex network, influential nodes, gravity model, neighborhood interaction, topological similarity