量化真实网络流中的有效信息交换
Quantifying efficient information exchange in real network flows
摘要：
网络科学使得能够有效地分析真实的互连系统，其特征在于拓扑结构和互连强度之间的复杂相互作用。众所周知，网络的拓扑结构影响其对故障或攻击的弹性以及其功能。交换信息对许多真实系统至关重要：互联网、交通网络和大脑都是重要的例子。尽管引入的措施的效率来分析网络流量，即拓扑结构的特点是加权连接，在这里，我们表明，他们未能捕捉到的链接存在和链接权重的组合信息。在这封信中，我们提出了一个基于物理基础的流量效率估计器，可以为每个加权网络计算，无论权重的规模和性质以及任何（缺失）元数据如何。值得注意的是，结果表明，我们的估计捕获的异质性流随着拓扑结构的差异和它的补充信息从几个经验系统，包括运输，贸易，移民和大脑网络的渗流分析。我们表明，削减最重的连接可能会增加系统的平均通信效率，因此，反直觉地，稀疏网络不一定是效率较低。值得注意的是，我们的估计器可以比较不同领域的网络的通信效率，而不需要考虑流量规模可能带来的陷阱。
英文摘要：
Network science enables the effective analysis of real interconnected systems, characterized by a complex interplay between topology and interconnections strength. It is well-known that the topology of a network affects its resilience to failures or attacks, as well as its functions. Exchanging information is crucial for many real systems: the internet, transportation networks and the brain are key examples. Despite the introduction of measures of efficiency to analyze network flows, i.e. topologies characterized by weighted connectivity, here we show that they fail to capture combined information of link existence and link weight. In this letter we propose a physically-grounded estimator of flow efficiency which can be computed for every weighted network, regardless from the scale and nature of weights and from any (missing) metadata. Remarkably, results show that our estimator captures the heterogeneity of flows along with topological differences and its complement information obtained from percolation analysis of several empirical systems, including transportation, trade, migrations, and brain networks. We show that cutting the heaviest connections may increase the average communication efficiency of the system and hence, counterintuively, a sparser network is not necessarily less efficient. Remarkably, our estimator enables the comparison of communication efficiency of networks arising from different fields, without the possible pitfalls deriving from the scale of flow.
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