A Polya urn approach to information filtering in complex networks
A Polya urn approach to information filtering in complex networks
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DOI：
10.1038/s41467-019-08667-3
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发表时间：
2019-02-14
影响因子：
16.6
通讯作者：
Livan, Giacomo
中科院分区：
文献类型：
--
作者：
Marcaccioli, Riccardo;Livan, Giacomo
越来越多的数据可用性要求在大型复杂的交互网络中过滤信息的技术。已经提出了许多方法，通过评估随机相互作用的零假设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，因为节点之间过去的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Polya瓮启发的过滤方法，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，以评估相对于给定网络本身的异质性，哪些链接具有统计显著性。我们提供了过滤器的完整表征，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。
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 Polya 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.