论文标题
高阶网络中的凝聚力和隔离
Cohesion and segregation in higher-order networks
论文作者
论文摘要
为了克服传统网络的局限性，网络科学界最近非常关注所谓的高阶网络，在这些高阶网络中，在其中与成对的互动建模。虽然学位分布和聚类是传统网络结构的最重要特征，但高阶网络呈现出几乎没有解决的其他两个基本属性：组大小分布和重叠。在这里，我研究了这些属性对网络结构的影响，重点是凝聚力和隔离（分裂和社区形成）。为此，我创建了具有配置模型版本的人工高阶网络，该网络将学位分配给节点和大小为组和大小与调谐参数$ p $重叠。违反直觉，结果表明，高频重叠有利于网络的内聚和隔离 - 网络变得更加模块化，甚至可以分解为几个组件，但具有紧密联系的社区。
Looking to overcome the limitations of traditional networks, the network science community has lately given much attention to the so-called higher-order networks, where group interactions are modeled alongside pairwise ones. While degree distribution and clustering are the most important features of traditional network structure, higher-order networks present two additional fundamental properties that are barely addressed: the group size distribution and overlaps. Here, I investigate the impact of these properties on the network structure, focusing on cohesion and segregation (fragmentation and community formation). For that, I create artificial higher-order networks with a version of the configuration model that assigns degree to nodes and size to groups and forms overlaps with a tuning parameter $p$. Counter-intuitively, the results show that a high frequency of overlaps favors both network cohesion and segregation -- the network becomes more modular and can even break into several components, but with tightly-knit communities.
