Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs.
Contact networks have small metric backbones that maintain community structure and are primary transmission subgraphs.
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DOI：
10.1371/journal.pcbi.1010854
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发表时间：
2023-03
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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社交网络的结构强烈影响着不同现象在人类社会中的传播，从信息的传播到传染病的传播。众所周知，异构连接强烈地有利于传播，但是社交网络中存在的冗余及其对传输鲁棒性的影响的精确表征仍然缺乏。度量主干解决了这个问题，度量主干是一个保持权重和连通性的子图，足以计算加权图的所有最短路径。这个子图是通过代数原理公理获得的，并且不需要基于空模型的统计采样。我们发现，在各种社交环境中从接近传感器获得的9个接触网络的度量骨干通常非常小，一个是原始图的49%，另一个是约6%至20%。这反映了令人惊讶的冗余量，并揭示了这些网络上的最短路径对随机攻击和故障非常鲁棒。我们还表明，度量骨干保留完整的原始接触网络的最短路径的分布，其中必须包括最短的社区间和社区内的距离，定义任何社区结构，是一个主要的子图的基础上纯扩散过程的流行病传播。这表明，社会联系网络的组织是基于大量的最短路径冗余，这塑造了流行病在人群中的传播。因此，度量主干是关于流行病传播、社交网络的鲁棒性以及依赖于复杂网络最短路径的任何通信动态的重要子图。传染病是通过社交网络在人群中传播的，当前的流行病和控制它的努力就是最好的说明。从人类接触数据中测量这种网络通常会产生嘈杂和密集的图表，需要简化以进行有效的分析，而不删除其基本特征。因此，识别一个主要的子图，保持社会互动结构和可能的传播途径是相关的研究流行病传播现象，以及制定干预策略，以阻止传播。在这里，我们提出并研究了度量骨干作为一个最佳的子图的稀疏化的社会接触网络的简单传播动力学的研究。我们证明，它是一个唯一的，代数原则的网络子图，保持所有的最短路径。我们还发现，在各种社会背景下从接近传感器获得的9个接触网络包含大量的冗余交互，这些冗余交互可以被移除，对社区结构和流行病传播的影响很小。这揭示了社交网络上的流行病传播对于随机交互移除是非常鲁棒的。然而，提取度量骨干子图揭示了哪些干预措施-战略性地消除特定的社会互动-可能会导致最大限度地阻碍流行病的传播。
The structure of social networks strongly affects how different phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its effect on the robustness of transmission is still lacking. This gap is addressed by the metric backbone, a weight- and connectivity-preserving subgraph that is sufficient to compute all shortest paths of weighted graphs. This subgraph is obtained via algebraically-principled axioms and does not require statistical sampling based on null-models. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We also show that the metric backbone preserves the full distribution of shortest paths of the original contact networks—which must include the shortest inter- and intra-community distances that define any community structure—and is a primary subgraph for epidemic transmission based on pure diffusion processes. This suggests that the organization of social contact networks is based on large amounts of shortest-path redundancy which shapes epidemic spread in human populations. Thus, the metric backbone is an important subgraph with regard to epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths. It is through social networks that contagious diseases spread in human populations, as best illustrated by the current pandemic and efforts to contain it. Measuring such networks from human contact data typically results in noisy and dense graphs that need to be simplified for effective analysis, without removal of their essential features. Thus, the identification of a primary subgraph that maintains the social interaction structure and likely transmission pathways is of relevance for studying epidemic spreading phenomena as well as devising intervention strategies to hinder spread. Here we propose and study the metric backbone as an optimal subgraph for sparsification of social contact networks in the study of simple spreading dynamics. We demonstrate that it is a unique, algebraically-principled network subgraph that preserves all shortest paths. We also discover that nine contact networks obtained from proximity sensors in a variety of social contexts contain large amounts of redundant interactions that can be removed with very little impact on community structure and epidemic spread. This reveals that epidemic spread on social networks is very robust to random interaction removal. However, extraction of the metric backbone subgraph reveals which interventions—strategic removal of specific social interactions—are likely to result in maximum impediment to epidemic spread.
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影响因子：
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作者：
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DOI：
10.1073/pnas.2022598118
发表时间：
2021-03-23
影响因子：
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通讯作者：
Rocha, Luis M.