Dynamic Signed Graph Learning
Dynamic Signed Graph Learning
复制标题
DOI：
10.1109/icassp49357.2023.10094914
复制
发表时间：
2023-06
期刊：
影响因子：
--
通讯作者：
Abdullah Karaaslanli;Selin Aviyente
中科院分区：
文献类型：
--
作者：
Abdullah Karaaslanli;Selin Aviyente
图信号处理（GSP）中的一个重要问题是从图的节点上的一组观测（即图信号）来推断未知图的拓扑。最近，图学习（GL）方法已经扩展到从时间图信号学习动态图。然而，现有的工作主要集中在无符号图，不能学习有符号图，这是重要的数据结构，可以表示节点的相似性和相异性。在本文中，我们提出了一个动态的有符号GL（dynSGL）方法的基础上的假设，（i）在每个时间点的信号是光滑的有符号图，即在两个节点连接的正（负）边的信号值是相似的（不相似的）和（ii）图结构的演变是平滑的跨时间。dynSGL的性能进行评估模拟数据，并显示具有更高的精度相比，静态签署和动态无符号GL技术。所提出的方法的金融数据集的应用程序提供了重要的见解，股票之间的相互作用随时间变化的变化。
An important problem in graph signal processing (GSP) is to infer the topology of an unknown graph from a set of observations on the nodes of the graph, i.e. graph signals. Recently, graph learning (GL) approaches have been extended to learn dynamic graphs from temporal graph signals. However, existing work primarily focuses on unsigned graphs and cannot learn signed graphs, which are important data structures that can represent the similarity and dissimilarity of the nodes. In this paper, we propose a dynamic signed GL (dynSGL) method based on the assumptions that (i) at each time point signals are smooth with respect to the signed graph, i.e. signal values at two nodes connected with a positive (negative) edge are similar (dissimilar) and (ii) evolution of the graph structures is smooth across time. The performance of dynSGL is evaluated on simulated data and shown to have higher accuracy compared to static signed and dynamic unsigned GL techniques. Application of the proposed method to a financial dataset gives important insights to the time-varying changes to the interactions between stocks.