{"title":"图神经网络在大规模网络性能评估中的应用","authors":"Cen Wang, N. Yoshikane, T. Tsuritani","doi":"10.23919/ONDM51796.2021.9492331","DOIUrl":null,"url":null,"abstract":"To quickly and accurately perform network evaluation, we propose a graph convolutional network-based performance evaluation method for ultralarge-scale networks. The learning results show that our method outperforms the fully connected network and convolutional neural network in the prediction error of the end-to-end latency and network throughput. In addition, we show that our method is significantly less time-consuming than traditional methods.","PeriodicalId":163553,"journal":{"name":"2021 International Conference on Optical Network Design and Modeling (ONDM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Usage of a Graph Neural Network for Large-Scale Network Performance Evaluation\",\"authors\":\"Cen Wang, N. Yoshikane, T. Tsuritani\",\"doi\":\"10.23919/ONDM51796.2021.9492331\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To quickly and accurately perform network evaluation, we propose a graph convolutional network-based performance evaluation method for ultralarge-scale networks. The learning results show that our method outperforms the fully connected network and convolutional neural network in the prediction error of the end-to-end latency and network throughput. In addition, we show that our method is significantly less time-consuming than traditional methods.\",\"PeriodicalId\":163553,\"journal\":{\"name\":\"2021 International Conference on Optical Network Design and Modeling (ONDM)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Optical Network Design and Modeling (ONDM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ONDM51796.2021.9492331\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Optical Network Design and Modeling (ONDM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ONDM51796.2021.9492331","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Usage of a Graph Neural Network for Large-Scale Network Performance Evaluation
To quickly and accurately perform network evaluation, we propose a graph convolutional network-based performance evaluation method for ultralarge-scale networks. The learning results show that our method outperforms the fully connected network and convolutional neural network in the prediction error of the end-to-end latency and network throughput. In addition, we show that our method is significantly less time-consuming than traditional methods.