基于信息级联的网络结构贝叶斯推断
Bayesian inference of network structure from information cascades
摘要：
传染过程与其传播的网络结构密切相关，了解这些结构对于理解和干预复杂的网络过程（如流行病和（错误）信息传播）至关重要。然而，利用传染数据来推断网络结构是一个具有挑战性的逆问题。特别是，在网络结构估计中必须有适当的不确定性度量，但在大多数机器学习方法中，这些度量在很大程度上被忽略了。我们提出了一个概率框架，该框架使用与观察到的动态相兼容的网络分布样本来产生网络和不确定性估计。我们证明了该方法使用著名的独立级联模型从网络P（G）的分布采样的条件下，观察一组感染C。我们通过使用分布中每个边缘的边缘概率来评估该方法的准确性，并展示了量化不确定性以提高估计和理解的好处，特别是在少量数据的情况下。
英文摘要：
Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures of uncertainty in network structure estimates, however these are largely ignored in most machine-learning approaches. We present a probabilistic framework that uses samples from the distribution of networks that are compatible with the dynamics observed to produce network and uncertainty estimates. We demonstrate the method using the well known independent cascade model to sample from the distribution of networks P(G) conditioned on the observation of a set of infections C. We evaluate the accuracy of the method by using the marginal probabilities of each edge in the distribution, and show the bene ts of quantifying uncertainty to improve estimates and understanding, particularly with small amounts of data.
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