The usage of social media around the world is ever-increasing. Social media statistics from 2019 show that there are 3.5 billion social media users worldwide. However, the existence of community structure renders the network vulnerable to attacks and large-scale losses. How does one comprehensively consider the multiple information sources and effectively evaluate the vulnerability of the community? To answer this question, we design a gravity-based community vulnerability evaluation (GBCVE) model for multiple information considerations. Specifically, we construct the community network by the Jensen_Shannon divergence and log-sigmoid transition function to show the relationship between communities. The number of edges inside community and outside of each community, as well as the gravity index are the three important factors used in this model for evaluating the community vulnerability. These three factors correspond to the interior information of the community, small-scale interaction relationship, and large-scale interaction relationship, respectively. A fuzzy ranking algorithm is then used to describe the vulnerability relationship between different communities, and the sensitivity of different weighting parameters is then analyzed by Sobol’ indices. We validate and demonstrate the applicability of our proposed community vulnerability evaluation method via three real-world complex network test examples. Our proposed model can be applied to find vulnerable components in a network to mitigate the influence of public opinions or natural disasters in real time. The community vulnerability evaluation results from our proposed model are expected to shed light on other properties of communities within social networks and have real-world applications across network science.

中文翻译：

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社交网络中基于重力的社区脆弱性评估模型：GBCVE


世界各地社交媒体的使用量不断增加。 2019年社交媒体统计数据显示，全球有35亿社交媒体用户。然而，社区结构的存在使得网络容易受到攻击并造成大规模损失。如何综合考虑多种信息来源，有效评估社区的脆弱性？为了回答这个问题，我们出于多种信息考虑，设计了基于重力的社区脆弱性评估（GBCVE）模型。具体来说，我们通过 Jensen_Shannon 散度和 log-sigmoid 转换函数构建社区网络来展示社区之间的关系。群落内部和群落外部的边数以及引力指数是该模型评估群落脆弱性的三个重要因素。这三个因素分别对应社区内部信息、小尺度交互关系、大尺度交互关系。然后采用模糊排序算法描述不同社区之间的脆弱性关系，并通过Sobol指数分析不同权重参数的敏感性。我们通过三个现实世界的复杂网络测试示例验证并证明了我们提出的社区脆弱性评估方法的适用性。我们提出的模型可用于查找网络中的脆弱组件，以实时减轻舆论或自然灾害的影响。我们提出的模型的社区脆弱性评估结果预计将揭示社交网络中社区的其他属性，并在整个网络科学中具有实际应用。