带符号有向复杂网络中的模体动力学
Motif Dynamics in Signed Directional Complex Networks
AI总结：
本文研究带符号有向复杂网络中的模体动力学，通过测量同时考虑链接符号和方向性的网络模体，分析模体数量随时间的变化，发现非循环模体与系统规模的相关性大于循环模体，并能揭示大学新生间的友谊关系。
摘要：
复杂网络随时间演化并改变其结构。特别是，这些网络中的链接同时具有符号和方向性。为了理解其结构原理，我们测量了网络模体，即在考虑两种链接属性的情况下，在随机网络中出现频率远超预期的模式。我们提出了模体动力学，这是一项研究模体数量变化的研究，并将其应用于开放演化网络模型和实证数据。我们证实，非循环模体与系统规模的相关性大于循环结构模体。此外，模体动力学能让我们深入了解大学新生之间的友谊关系。
英文摘要：
Complex networks evolve and vary their structure as time goes by. In particular, the links in those networks have both a sign and a directionality. To understand their structural principles, we measure the network motifs, which are patterns that appear much more than one would expect in randomized networks, considering both link properties. We propose motif dynamics, which is a study to investigate the change in the number of motifs, and applied the motif dynamics to an open evolving network model and empirical data. We confirm that a non-cyclic motif has a greater correlation with the system size than a cyclic structural motif. Furthermore, the motif dynamics can give us insight into the friendship between freshmen in a university.
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