Kuang Junyao, Scoglio Caterina
Department of Electrical and Computer Engineering, Kansas State University, Manhattan, Kansas 66506, USA.
Phys Rev E. 2021 Aug;104(2-1):024301. doi: 10.1103/PhysRevE.104.024301.
From social networks to biological networks, different types of interactions among the same set of nodes characterize distinct layers, which are termed multilayer networks. Within a multilayer network, some layers, confirmed through different experiments, could be structurally similar and interdependent. In this paper, we propose a maximum a posteriori-based method to study and reconstruct the structure of a target layer in a multilayer network. Nodes within the target layer are characterized by vectors, which are employed to compute edge weights. Further, to detect structurally similar layers, we propose a method for comparing networks based on the eigenvector centrality. Using similar layers, we obtain the parameters of the conjugate prior. With this maximum a posteriori algorithm, we can reconstruct the target layer and predict missing links. We test the method on two real multilayer networks, and the results show that the maximum a posteriori estimation is promising in reconstructing the target layer even when a large number of links is missing.
从社交网络到生物网络，同一组节点之间不同类型的相互作用表征了不同的层，这些层被称为多层网络。在多层网络中，通过不同实验确认的一些层在结构上可能相似且相互依存。在本文中，我们提出了一种基于最大后验概率的方法来研究和重建多层网络中目标层的结构。目标层内的节点由向量表征，这些向量用于计算边权重。此外，为了检测结构相似的层，我们提出了一种基于特征向量中心性的网络比较方法。利用相似层，我们获得共轭先验的参数。通过这种最大后验概率算法，我们可以重建目标层并预测缺失的链接。我们在两个真实的多层网络上测试了该方法，结果表明即使大量链接缺失，最大后验概率估计在重建目标层方面也很有前景。
