标题： 超模ularity与超图中的社区检测 显示英文标题

标题： Hypermodularity and community detection in hypergraphs

摘要： 许多网络化系统具有非平凡社区的结构，这通常对应于它们的功能模块。 这些社区已在现实世界中的生物、社交和技术系统以及相应的合成模型中被检测到。 尽管已经投入了大量努力来开发传统网络中的社区检测方法，但对具有高阶相互作用的网络中的社区结构的研究仍不够广泛。 在本文中，我们引入了一种高阶网络超模块性的形式化方法，使我们能够使用谱方法在超图中检测社区结构。 我们将这种方法应用于合成随机网络以及现实世界的数据，结果表明它能够反映所建模交互的本质和动态，从而成为从复杂高阶数据集中提取隐藏信息的有用工具。 显示英文摘要

摘要： Numerous networked systems feature a structure of nontrivial communities, which often correspond to their functional modules. Such communities have been detected in real-world biological, social and technological systems, as well as in synthetic models thereof. While much effort has been devoted to developing methods for community detection in traditional networks, the study of community structure in networks with higher-order interactions is still not as extensively explored. In this article, we introduce a formalism for the hypermodularity of higher-order networks that allows us to use spectral methods to detect community structures in hypergraphs. We apply this approach to synthetic random networks as well as to real-world data, showing that it produces results that reflect the nature and the dynamics of the interactions modelled, thereby constituting a valuable tool for the extraction of hidden information from complex higher-order data sets.