In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to model the dependence of node attributes on core scores of the nodes of a graph through a latent graph structure. Using the proposed model, we jointly infer a sparse graph and nodal core scores that induce dense (sparse) connections in core (respectively, peripheral) parts of the network. Numerical experiments on a variety of real-world data indicate that the proposed method learns a core-periphery structured graph from node attributes alone, while simultaneously learning core score assignments that agree well with existing works that estimate core scores using graph as input and ignoring commonly available node attributes.

翻译：在本文中,我们侧重于学习带有核心外观结构结构的稀有图表。 我们提出了一个与核心外观结构网络相关的数据的基因化模型, 以模拟结点属性对图表节点核心分数的依赖性, 通过一个潜在的图形结构进行模型。 我们使用拟议的模型, 联合推导一个稀有的图表和结点核心分数, 以诱导网络核心部分( 分别是外围的) 的密( 粗) 连接。 对各种真实世界数据的数值实验表明, 拟议的方法只从节点属性中学习核心外观结构图表, 同时学习与现有工作一致的核心得分分配, 以图表作为输入来估计核心得分, 并忽略常见的节点属性。