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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v7i6.5668</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Manifold Structure Analysis of Tactical Network Traffic Matrix Based on Maximum Variance Unfolding Algorithm</title><url>https://artdesignp.com/journal/JERA/7/6/10.26689/jera.v7i6.5668</url><author>ShiHao,WangGuofeng,WangRouxi,YangJinshan,ShangKaishuan</author><pub-date pub-type="publication-year"><year>2023</year></pub-date><volume>7</volume><issue>6</issue><history><date date-type="pub"><published-time>2023-11-29</published-time></date></history><abstract>As modern weapons and equipment undergo increasing levels of informatization, intelligence, and networking, the topology and traffic characteristics of battlefield data networks built with tactical data links are becoming progressively complex. In this paper, we employ a traffic matrix to model the tactical data link network. We propose a method that utilizes the Maximum Variance Unfolding (MVU) algorithm to conduct nonlinear dimensionality reduction analysis on high-dimensional open network traffic matrix datasets. This approach introduces novel ideas and methods for future applications, including traffic prediction and anomaly analysis in real battlefield network environments.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Papagiannaki N, Taft N, Zhang Z, et al., 2003, 22nd Annual Joint Conference of the IEEE Computer and Communications Societies, March 30–April 3, 2003: Long-Term Forecasting of Internet Backbone Traffic: Observations and Initial Models. 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