New England Complex Systems Institute, Cambridge, MA, 02139, USA.
University of Massachusetts Dartmouth, Dartmouth, MA, 02747-2300, USA.
Sci Rep. 2020 Oct 22;10(1):18137. doi: 10.1038/s41598-020-75221-3.
Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs-synchronizability-and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.
理解网络子图所执行的功能对于揭示各种复杂网络的组织原则很重要。在这里，我们在协作解决问题的背景下研究这个问题，协作解决问题是从工程和医学到经济学和社会规划等各种领域的核心。我们分析了各种真实问题解决网络中所有三节点和四节点子图的频率。结果表明，网络子图的动态特性——同步性——与这些子图在解决问题网络中的频率和重要性之间存在很强的关联。特别是，我们表明，高度同步的子图在网络中过度表示，而同步性差的子图表示不足，这表明动态特性会影响它们的普遍性，从而影响网络的全局结构。我们提出了这样一种可能性，即有利于更同步的子图的选择压力可能解释了它们在解决问题的网络中丰富存在的原因。实证结果还表明，不相关的解决问题的网络显示出非常相似的局部网络结构，这意味着网络子图可能代表了组织惯例，这些惯例可以更好地协调和控制解决问题的活动。这些发现也可能对理解其他信息处理网络（包括生物和社会网络）中网络子图的功能具有潜在影响。
