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Asymmetric Disassembly And Robustness In Declining Networks

机译:衰落网络中的不对称分解和鲁棒性

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Mechanisms that enable declining networks to avert structural collapse and performance degradation are not well understood. This knowledge gap reflects a shortage of data on declining networks and an emphasis on models of network growth. Analyzing > 700,000 transactions between firms in the New York garment industry over 19 years, we tracked this network's decline and measured how its topology and global performance evolved. We find that favoring asymmetric (disassortative) links is key to preserving the topology and functionality of the declining network. Based on our findings, we tested a model of network decline that combines an asymmetric disassembly process for contraction with a preferential attachment process for regrowth. Our simulation results indicate that the model can explain robustness under decline even if the total population of nodes contracts by more than an order of magnitude, in line with our observations for the empirical network. These findings suggest that disassembly mechanisms are not simply assembly mechanisms in reverse and that our model is relevant to understanding the process of decline and collapse in a broad range of biological, technological, and financial networks.
机译:人们对启用下降网络避免结构崩溃和性能下降的机制了解得很少。这种知识差距反映出网络下降的数据不足,并且强调了网络增长的模型。通过分析19年来纽约服装行业公司之间的700,000笔交易,我们跟踪了该网络的下降情况,并测量了其拓扑结构和全球绩效的演变情况。我们发现,偏爱非对称(离散)链接是保留下降网络的拓扑和功能的关键。根据我们的发现,我们测试了一种网络衰退模型,该模型将不对称分解过程与收缩过程和优先附着过程结合在一起,以实现再生。我们的仿真结果表明,即使节点总数减少了一个数量级,该模型也可以解释在下降情况下的鲁棒性,这与我们对经验网络的观察一致。这些发现表明,拆卸机制不仅仅是简单的反向组装机制,而且我们的模型与理解广泛的生物,技术和金融网络中的衰退和崩溃过程有关。

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