An Entropy-Based Gravity Model for Influential Spreaders Identification in Complex Networks
Yong Liu, Zijun Cheng, Xiaoqin Li, Zongshui Wang
Abstract
The mining of key nodes is an important topic in complex network research, which can help identify influencers. The study is necessary for blocking the spread of epidemics, controlling public opinion, and managing transportation. The techniques thus far suggested have a lot of drawbacks; they either depend on the regional distribution of nodes or the global character of the network. The gravity formula based on node information is a good mathematical model that can represent the magnitude of attraction between nodes. However, the gravity model requires less node information and has limitations. In this study, we propose a gravity model based on Shannon entropy to effectively address the aforementioned issues. The spreading probability method is employed to enhance the model’s functionality and applicability. Through testing, it has been determined that the suggested model is a good alternative to the gravity model for selecting influential nodes.