A generalized gravity model for influential spreaders identification in complex networks

How to identify influential spreaders in complex networks is still an open issue in network science. Many approaches from different perspectives have been proposed to identify vital nodes in complex networks. In these models, gravity model is an effective model to find vital nodes based on local information and path information. However, gravity model just uses degree of the node to judge local information, which is not precise. To address this issue, a generalized gravity model is proposed in this paper. Generalized gravity model measures local information from both local clustering coefficient and degree of each node, which is more comprehensive. Also, parameter alpha can be modified in different applications to get better performance. Generalized gravity model can degenerate into gravity model when alpha = 0. Promising results from experiments on four real-world networks demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

A generalized gravity model was proposed to identify influential spreaders in complex networks, which measures local information from both local clustering coefficient and degree of each node. The experimental results demonstrate the effectiveness of this method.