用模块生命力度量节点对社区结构的贡献
Measuring Node Contribution to Community Structure with Modularity Vitality
摘要：
社区感知中心性是网络科学中的一个新兴研究领域，关注节点对社区结构的重要性。测度是网络结构和给定分区的函数。以前的方法扩展了经典的中心性措施，占社区结构与社区发现理论的联系不大。相比之下，我们提出了集群质量的活力措施，即，模块化活力，这是一个社区意识的衡量标准，它充分基于中心性和社区发现理论。模块化活力量化了节点对社区结构的积极和消极贡献，表明节点作为社区桥梁或枢纽的作用。我们推导出一个计算效率高的方法，计算模块化生命力的所有节点在O（M + NC）的时间，其中C是社区的数量。我们通过移除中心节点来系统地分割网络，并发现模块化活力始终优于现有的社区感知中心性措施。在百万节点的基础设施网络上，模块化活力的效率是次优方法的8倍以上。这一结果并不适用于社交媒体通信网络，社交媒体通信网络对所有社区感知中心攻击都表现出极强的鲁棒性。这种稳健性表明，以用户为基础的干预措施，以减轻错误信息的传播将是无效的。最后，我们证明了模块化的生命力提供了一种新的方法来社区欺骗。
英文摘要：
Community-aware centrality is an emerging research area in network science concerned with the importance of nodes in relation to community structure. Measures are a function of a network's structure and a given partition. Previous approaches extend classical centrality measures to account for community structure with little connection to community detection theory. In contrast, we propose cluster-quality vitality measures, i.e., modularity vitality, a community-aware measure which is well-grounded in both centrality and community detection theory. Modularity vitality quantifies positive and negative contributions to community structure, which indicate a node's role as a community bridge or hub. We derive a computationally efficient method of calculating modularity vitality for all nodes in O(M + NC) time, where C is the number of communities. We systematically fragment networks by removing central nodes, and find that modularity vitality consistently outperforms existing community-aware centrality measures. Modularity vitality is over 8 times more effective than the next-best method on a million-node infrastructure network. This result does not generalize to social media communication networks, which exhibit extreme robustness to all community-aware centrality attacks. This robustness suggests that user-based interventions to mitigate misinformation diffusion will be ineffective. Finally, we demonstrate that modularity vitality provides a new approach to community-deception.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
