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Heterogeneous node copying from hidden network structure
Communications Physics ( IF 5.5 ) Pub Date : 2021-09-02 , DOI: 10.1038/s42005-021-00694-1
Max Falkenberg 1
Affiliation  

Node copying is an important mechanism for network formation, yet most models assume uniform copying rules. Motivated by observations of heterogeneous triadic closure in real networks, we introduce the concept of a hidden network model—a generative two-layer model in which an observed network evolves according to the structure of an underlying hidden layer—and apply the framework to a model of heterogeneous copying. Framed in a social context, these two layers represent a node’s inner social circle, and wider social circle, such that the model can bias copying probabilities towards, or against, a node’s inner circle of friends. Comparing the case of extreme inner circle bias to an equivalent model with uniform copying, we find that heterogeneous copying suppresses the power-law degree distributions commonly seen in copying models, and results in networks with much higher clustering than even the most optimum scenario for uniform copying. Similarly large clustering values are found in real collaboration networks, lending empirical support to the mechanism.



中文翻译:

从隐藏网络结构复制异构节点

节点复制是网络形成的重要机制,但大多数模型都假设有统一的复制规则。受对真实网络中异构三元闭包的观察的启发,我们引入了隐藏网络模型的概念——一个生成的两层模型,其中观察到的网络根据底层隐藏层的结构进化——并将该框架应用于模型异构复制。在社交环境中,这两层代表一个节点的内部社交圈和更广泛的社交圈,因此该模型可以将复制概率偏向或反对节点的内部朋友圈。将极端内圈偏差的情况与具有均匀复制的等效模型进行比较,我们发现异构复制抑制了复制模型中常见的幂律度分布,并导致网络具有比均匀复制的最佳场景更高的集群。在真实的协作网络中也发现了类似的大聚类值,为该机制提供了实证支持。

更新日期:2021-09-02
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