A Novel Spectral Graph Distance Measure and Its Applications in Biomedical Signal Processing

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-01-29 DOI:10.1109/TSIPN.2025.3536085

Jyoti Maheshwari;Shiv Dutt Joshi;Tapan K Gandhi

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引用次数: 0

Abstract

In many real time scenarios, it becomes necessary to compare complex networks. Graph distances measure the degree of similarity between any two networks. We conjecture that construction of the edge weight matrix of any network should consider the underlying physics of the problem. In addition to that, the selection of eigenvectors chosen for analysis also play an important role in comparing any two networks. In this paper, we explore the most naturally formed networks in various real time applications, such as tracking the transitions of brain states and real time seizure onset detection, and propose a graph distance measure based on the spectral characteristics of the edge weight matrix. We have conducted several experiments to show the efficacy of the proposed graph distance measure. It has an application in comparing any two brain networks while studying brain dynamics. Our results clearly show that the proposed graph distance outperforms the existing graph distances.

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在许多实时场景中，有必要对复杂的网络进行比较。图距离测量任意两个网络之间的相似程度。我们推测，构建任何网络的边缘权重矩阵都应考虑问题的基本物理原理。此外，选择用于分析的特征向量在比较任何两个网络时也起着重要作用。在本文中，我们探讨了在各种实时应用中最自然形成的网络，如跟踪大脑状态的转换和实时癫痫发作检测，并提出了一种基于边缘权重矩阵频谱特征的图距离测量方法。我们进行了多项实验来证明所提出的图距离度量的有效性。它适用于在研究大脑动态时比较任意两个大脑网络。我们的结果清楚地表明，所提出的图距离优于现有的图距离。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.