基于熵的复杂网络影响传播者识别重力模型

Yong Liu, Zijun Cheng, Xiaoqin Li, Zongshui Wang
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引用次数: 1

摘要

关键节点的挖掘是复杂网络研究中的一个重要课题,它有助于识别影响者。这项研究对于阻止流行病的传播、控制舆论、管理交通都是必要的。迄今为止建议的技术有很多缺点;它们要么取决于节点的区域分布,要么取决于网络的全球特征。基于节点信息的引力公式是一个很好的数学模型,可以表示节点间的引力大小。然而,重力模型需要较少的节点信息,有其局限性。在本研究中,我们提出了一个基于香农熵的重力模型来有效地解决上述问题。采用扩展概率方法,增强了模型的功能性和适用性。通过测试,确定该模型可以很好地替代重力模型来选择影响节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Entropy-Based Gravity Model for Influential Spreaders Identification in Complex Networks
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.
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CiteScore
2.80
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