Department of Computer Science, University College London, 66-72 Gower Street, London, WC1E 6EA, UK.
Systemic Risk Centre, London School of Economics and Political Sciences, Houghton Street, London, WC2A 2AE, UK.
Nat Commun. 2019 Feb 14;10(1):745. doi: 10.1038/s41467-019-08667-3.
The increasing availability of data demands for techniques to filter information in large complex networks of interactions. A number of approaches have been proposed to extract network backbones by assessing the statistical significance of links against null hypotheses of random interaction. Yet, it is well known that the growth of most real-world networks is non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a filtering methodology inspired by the Pólya urn, a combinatorial model driven by a self-reinforcement mechanism, which relies on a family of null hypotheses that can be calibrated to assess which links are statistically significant with respect to a given network's own heterogeneity. We provide a full characterization of the filter, and show that it selects links based on a non-trivial interplay between their local importance and the importance of the nodes they belong to.
随着数据的日益丰富，人们需要技术来筛选大型复杂交互网络中的信息。已经提出了许多方法来通过评估与随机交互的零假设相比链接的统计显著性来提取网络骨干。然而，众所周知，大多数现实世界网络的增长是非随机的，因为节点之间的过去交互通常会增加进一步交互的可能性。在这里，我们提出了一种受 Pólya urn 启发的过滤方法，这是一种由自我强化机制驱动的组合模型，它依赖于一组可以校准的零假设，以评估哪些链接在统计上与给定网络的自身异质性显著。我们对过滤器进行了全面的描述，并表明它根据它们的局部重要性和它们所属节点的重要性之间的非平凡相互作用来选择链接。
