Laboratory for Machine Learning and Knowledge Representations, Rudjer Bošković Institute, Zagreb, Croatia.
Computational Social Science, ETH Zürich, Clausiusstraße 50, 8092, Zürich, Switzerland.
Sci Rep. 2018 Apr 26;8(1):6562. doi: 10.1038/s41598-018-24648-w.
We present a framework to simulate SIR processes on networks using weighted shortest paths. Our framework maps the SIR dynamics to weights assigned to the edges of the network, which can be done for Markovian and non-Markovian processes alike. The weights represent the propagation time between the adjacent nodes for a particular realization. We simulate the dynamics by constructing an ensemble of such realizations, which can be done by using a Markov Chain Monte Carlo method or by direct sampling. The former provides a runtime advantage when realizations from all possible sources are computed as the weighted shortest paths can be re-calculated more efficiently. We apply our framework to three empirical networks and analyze the expected propagation time between all pairs of nodes. Furthermore, we have employed our framework to perform efficient source detection and to improve strategies for time-critical vaccination.
我们提出了一个使用加权最短路径模拟网络上 SIR 过程的框架。我们的框架将 SIR 动力学映射到网络边的权重上，这可以用于马尔可夫和非马尔可夫过程。权重表示特定实现中相邻节点之间的传播时间。我们通过构建这样的实现集合来模拟动力学，这可以通过使用马尔可夫链蒙特卡罗方法或直接采样来完成。当从所有可能的源计算实现时，前者具有运行时优势，因为可以更有效地重新计算加权最短路径。我们将我们的框架应用于三个经验网络，并分析所有节点对之间的预期传播时间。此外，我们还利用我们的框架来进行有效的源检测，并改进对时间关键型疫苗接种的策略。
