网络上的层级团队结构和多维定位（或筒仓）
Hierarchical team structure and multidimensional localization (or siloing) on networks
摘要：
当组织互动网络的结构特性限制信息的传播时，就会出现知识孤岛。众所周知，这些结构性障碍在不同的规模上有多种形式--稀疏组织中的枢纽、大型密集团队或全球核心-外围结构--但我们缺乏对这些不同结构如何相互作用的理解。在这里，我们弥合了传播动力学本地化的数学文献和组织互动网络中知识孤岛的更多应用文献之间的差距。为此，我们引入了一个新的模型，该模型考虑了团队的分层结构，以揭示一种新形式的分层本地化（即，信息在组织的顶部或中心的局部化）并研究其与已知的介观局部化现象的相互作用（即，大组中信息的定位），$k$ -核心定位（即，围绕更密集的$k$ -核心）和集线器定位（即，高级别的明星）。我们还包括一个复杂的传染机制，通过考虑一个一般的感染内核，它可以取决于层次（影响力），程度（流行），传染性邻居（社会强化）或团队规模（重要性）。这个通用模型使我们能够研究复杂的组织互动网络中的信息孤岛的多方面现象，并为新的优化问题打开了大门，以促进或阻碍不同的本地化制度的出现。
英文摘要：
Knowledge silos emerge when structural properties of organizational interaction networks limit the diffusion of information. These structural barriers are known to take many forms at different scales - hubs in otherwise sparse organisations, large dense teams, or global core-periphery structure - but we lack an understanding of how these different structures interact. Here we bridge the gap between the mathematical literature on localization of spreading dynamics and the more applied literature on knowledge silos in organizational interaction networks. To do so, we introduce a new model that considers a layered structure of teams to unveil a new form of hierarchical localization (i.e., the localization of information at the top or center of an organization) and study its interplay with known phenomena of mesoscopic localization (i.e., the localization of information in large groups), $k$-core localization (i.e., around denser $k$-cores) and hub localization (i.e., around high degree stars). We also include a complex contagion mechanism by considering a general infection kernel which can depend on hierarchical level (influence), degree (popularity), infectious neighbors (social reinforcement) or team size (importance). This general model allows us to study the multifaceted phenomenon of information siloing in complex organizational interaction networks and opens the door to new optimization problems to promote or hinder the emergence of different localization regimes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
