基于集合覆盖的因果网络重构
Reconstruction of Causal Networks by Set Covering
摘要：
我们提出了一种重建网络的方法，基于随机分支过程访问的节点的顺序。我们的算法重建了一个最小尺寸的网络，以确保与数据的一致性。至关重要的是，我们表明，全球一致性的数据可以实现通过纯粹的本地考虑，依次推断每个节点的邻域。为每个单独的节点解决的优化问题可以简化为集合覆盖问题，这是已知的NP难，但在实践中可以很好地近似。然后，我们扩展我们的方法来考虑嘈杂的数据，基于最小描述长度原则。我们证明了我们的算法上的合成数据，由一个类似SIR的流行病学模型。
英文摘要：
We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local considerations, inferring the neighbourhood of each node in turn. The optimisation problem solved for each individual node can be reduced to a Set Covering Problem, which is known to be NP-hard but can be approximated well in practice. We then extend our approach to account for noisy data, based on the Minimum Description Length principle. We demonstrate our algorithms on synthetic data, generated by an SIR-like epidemiological model.
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