群体的重复性抑制高阶相互作用下的信息传播
The recurrence of groups inhibits the information spreading under higher-order interactions
AI总结：
针对群体形成驱动机制及高阶相互作用对面对面互动网络信息传播的影响不明问题，本研究发现群体重复模式，扩展力导向运动（FDM）模型重现该模式及关键特性，证明三角形群体重复性抑制信息传播且高阶相互作用加剧此现象，推进群体形成理解并为相关研究开辟新途径。
摘要：
将社会系统建模为基于个体间成对相互作用的网络，为理解其动力学机制提供了宝贵见解。然而，大多数社会互动发生在个体群体内部，具有高阶结构特征。群体形成的驱动机制以及源于群体动力学的高阶相互作用对面对面互动网络中信息传播的影响仍未得到充分理解。本研究中，我们分析了一些代表性的人类面对面互动数据，发现了群体的重复模式。此外，我们扩展了力导向运动（FDM）模型，引入隐藏空间内相似距离产生的力，以重现重复群体模式和面对面互动网络的诸多关键特性。我们还证明FDM模型能有效预测高阶相互作用下的信息传播行为。最后，结果表明三角形群体的重复性抑制面对面互动网络中的信息传播，且高阶相互作用会加剧这一现象。这些发现显著推进了对群体形成的理解，可能为群体互动对信息传播过程影响的研究开辟新途径。
英文摘要：
Modeling social systems as networks based on pairwise interactions between individuals offers valuable insights into the mechanisms underlying their dynamics. However, the majority of social interactions occur within groups of individuals, characterized by higher-order structures. The mechanisms driving group formation and the impact of higher-order interactions, which arise from group dynamics, on information spreading in face-to-face interaction networks remain insufficiently understood. In this study, we examine some representative human face-to-face interaction data and find the recurrent patterns of groups. Moreover, we extend the force-directed motion (FDM) model with the forces derived from similarity distances within a hidden space to reproduce the recurrent group patterns and many key properties of face-to-face interaction networks. Furthermore, we demonstrate that the FDM model effectively predicts information-spreading behaviors under higher-order interactions. Finally, our results reveal that the recurrence of triangular groups inhibits the spread of information in face-to-face interaction networks, and the higher-order interactions will make this phenomenon more pronounced. These findings represent a significant advancement in the understanding of group formation and may open new avenues for research into the effects of group interactions on information propagation processes.
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