{"title":"On the tree-depth and tree-width in heterogeneous random graphs","authors":"Y. Shang","doi":"10.3792/pjaa.98.015","DOIUrl":null,"url":null,"abstract":": In this note, we investigate the tree-depth and tree-width in a heterogeneous random graph obtained by including each edge e ij ð i 6¼ j Þ of a complete graph K n over n vertices independently with probability p n ð e ij Þ . When the sequence of edge probabilities satisfies some density assumptions, we show both tree-depth and tree-width are of linear size with high probability. Moreover, we extend the method to random weighted graphs with non-identical edge weights and capture the conditions under which with high probability the weighted tree-depth is bounded by a constant.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.3792/pjaa.98.015","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
: In this note, we investigate the tree-depth and tree-width in a heterogeneous random graph obtained by including each edge e ij ð i 6¼ j Þ of a complete graph K n over n vertices independently with probability p n ð e ij Þ . When the sequence of edge probabilities satisfies some density assumptions, we show both tree-depth and tree-width are of linear size with high probability. Moreover, we extend the method to random weighted graphs with non-identical edge weights and capture the conditions under which with high probability the weighted tree-depth is bounded by a constant.
在这篇文章中,我们研究了一个异构随机图的树深度和树宽度,该图是通过以概率p n ð e ij Þ独立包含K n / n个顶点的完全图的每个边e ij ð i 6¼j Þ获得的。当边缘概率序列满足一定的密度假设时,我们证明了树深和树宽都具有高概率的线性大小。此外,我们将该方法推广到具有不同边权的随机加权图中,并捕获了加权树深度有一个常数有界的高概率条件。