Title: Node metadata can produce predictability transitions in network inference problems Show Chinese title

Title: 节点元数据可以在网络推断问题中产生可预测性转换

Abstract: Network inference is the process of learning the properties of complex networks from data. Besides using information about known links in the network, node attributes and other forms of network metadata can help to solve network inference problems. Indeed, several approaches have been proposed to introduce metadata into probabilistic network models and to use them to make better inferences. However, we know little about the effect of such metadata in the inference process. Here, we investigate this issue. We find that, rather than affecting inference gradually, adding metadata causes abrupt transitions in the inference process and in our ability to make accurate predictions, from a situation in which metadata does not play any role to a situation in which metadata completely dominates the inference process. When network data and metadata are partly correlated, metadata optimally contributes to the inference process at the transition between data-dominated and metadata-dominated regimes. Show Chinese abstract

Abstract: 网络推断是从数据中学习复杂网络的属性的过程。 除了使用网络中已知链接的信息外，节点属性和其他形式的网络元数据可以帮助解决网络推断问题。 事实上，已经提出了一些方法将元数据引入概率网络模型，并利用它们进行更好的推断。 然而，我们对这种元数据在推断过程中的影响了解甚少。 在这里，我们研究了这个问题。 我们发现，添加元数据会导致推断过程和我们做出准确预测的能力出现突然的转变，从元数据没有任何作用的情况转变为元数据完全主导推断过程的情况。 当网络数据和元数据部分相关时，元数据在数据主导和元数据主导制度之间的转变中最优地贡献于推断过程。