具有核心-外围结构的稀疏图学习
Learning Sparse Graphs with a Core-periphery Structure
摘要：
在本文中，我们专注于学习具有核心-外围结构的稀疏图。我们提出了一个生成模型的数据与核心-外围结构的网络模型的节点属性的依赖关系的核心分数的图通过一个潜在的图结构。使用所提出的模型，我们共同推断稀疏图和节点的核心分数，诱导密集（稀疏）连接的核心（分别，外围）部分的网络。对各种真实数据的数值实验表明，该方法仅从节点属性学习核心-外围结构图，同时学习核心分数分配，这与现有的使用图作为输入并忽略常用节点属性来估计核心分数的工作一致。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
