Latent space models for multiplex networks with shared structure
Latent space models for multiplex networks with shared structure
复制标题
具有共享结构的多重网络的潜在空间模型
DOI：
10.1093/biomet/asab058
复制
发表时间：
2021
期刊：
影响因子：
2.7
通讯作者：
Zhu, J
中科院分区：
文献类型：
--
作者：
MacDonald, P W;Levina, E;Zhu, J
潜空间模型经常用于单层网络的建模，包括许多流行的特殊情况，如随机块模型和随机点积图。然而，它们并不适合更复杂的网络结构，这在实践中变得越来越普遍。本文提出了一种新的多路复用网络的潜在空间模型，在共享节点集上观察到的多个异构网络。多重网络可以表示具有共享节点标签的网络样本、随时间演进的网络或具有多种类型的边的网络。该模型的主要特点是，它从数据中学习网络结构中有多少是在层之间共享的，并在适当的情况下跨层共享信息。我们建立可识别性，开发了一个拟合过程，结合使用凸优化与核范数的惩罚，并证明了保证恢复的潜在位置之间的共享和个人的潜在子空间有足够的分离。我们比较的模型与竞争的方法在文献中的模拟网络和一个多元化的网络描述的全球农产品贸易。
Latent space models are frequently used for modelling single-layer networks and include many popular special cases, such as the stochastic block model and the random dot product graph. However, they are not well developed for more complex network structures, which are becoming increasingly common in practice. In this article we propose a new latent space model for multiplex networks, i.e., multiple heterogeneous networks observed on a shared node set. Multiplex networks can represent a network sample with shared node labels, a network evolving over time, or a network with multiple types of edges. The key feature of the proposed model is that it learns from data how much of the network structure is shared between layers and pools information across layers as appropriate. We establish identifiability, develop a fitting procedure using convex optimization in combination with a nuclear-norm penalty, and prove a guarantee of recovery for the latent positions provided there is sufficient separation between the shared and the individual latent subspaces. We compare the model with competing methods in the literature on simulated networks and on a multiplex network describing the worldwide trade of agricultural products.
DOI：
--
发表时间：
2017-09
期刊：
J. Mach. Learn. Res.
影响因子：
--
作者：
A. Athreya;D. E. Fishkind;M. Tang;C. Priebe;Youngser Park;J. Vogelstein;Keith D. Levin;V. Lyzinski;Yichen Qin;D. Sussman
通讯作者：
A. Athreya;D. E. Fishkind;M. Tang;C. Priebe;Youngser Park;J. Vogelstein;Keith D. Levin;V. Lyzinski;Yichen Qin;D. Sussman
DOI：
10.1111/rssb.12509
发表时间：
2017-09
期刊：
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
影响因子：
--
作者：
Patrick Rubin-Delanchy;C. Priebe;M. Tang;Joshua Cape
通讯作者：
Patrick Rubin-Delanchy;C. Priebe;M. Tang;Joshua Cape
影响因子：
2.7
作者：
Li, Tianxi;Levina, Elizaveta;Zhu, Ji
通讯作者：
Zhu, Ji