Learning the Structure of Hub Network Based on Graph Model

基于图模型的Hub网络的结构学习

¹ School of Mathematics, Northwest University, Xi'an 710127, China
² Xi'an Satellite Control Centre, Xi'an 710043, China
³ State Key Laboratory of Astronautic Dynamics, Xi'an 710043, China

Received: 28 October 2018

Abstract

In this paper, we focus on the structure learning problem of the hub network. In the neighborhood selection framework, we use the L₁ and L₂ regularizers to incorporate the sparse and group prior of the hub network, so as to make the network easier to generate Hub. We employ the coordinate descent algorithm to solve the resulting model. Simulation and real data analysis show that the proposed method is effective and applicable in parameter estimation and model selection, and results illustrate the influence ability of the control parameter on the model.

摘要

聚焦于具有Hub的网络结构学习问题。在邻域选择框架下，基于Hub网络的特点在模型中加入L₁和L₂正则子，从而分别引入网络的稀疏性先验和Hub网络的组先验，使所得网络更容易产生Hub。对于所得模型，采用坐标下降法求解。模拟数据和实际数据实验表明所提模型在参数估计、模型选择方面的有效性和实用性，并说明了调控参数对模型的影响。