Dynamical processes running on different networks behave differently, which makes the reconstruction of the underlying network from dynamical observations possible. However, to what level of detail the network properties can be determined from incomplete measurements of the dynamical process is still an open question. In this paper, we focus on the problem of inferring the properties of the underlying network from the dynamics of a susceptible-infected-susceptible epidemic and we assume that only a time series of the epidemic prevalence, i.e., the average fraction of infected nodes, is given. We find that some of the network metrics, namely those that are sensitive to the epidemic prevalence, can be roughly inferred if the network type is known. A simulated annealing link-rewiring algorithm, called SARA, is proposed to obtain an optimized network whose prevalence is close to the benchmark. The output of the algorithm is applied to classify the network types.

中文翻译：

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根据流行程度推断网络属性

在不同网络上运行的动态过程的行为有所不同，这使得从动态观察中重建基础网络成为可能。但是，从动态过程的不完整测量中可以确定网络属性的详细程度仍是一个悬而未决的问题。在本文中，我们着重于从易感性感染易感性流行病的动力学推断基础网络的特性的问题，并假设仅流行病的流行时间序列，即感染节点的平均比例，给出。我们发现，如果网络类型已知，则可以粗略地推论一些网络指标，即那些对流行病流行敏感的指标。一种称为SARA的模拟退火链接重排算法 为了获得一种优化的网络，该网络的患病率接近基准。该算法的输出用于对网络类型进行分类。