{"title":"识别社交网络的能力","authors":"","doi":"10.3103/s0278641924010084","DOIUrl":null,"url":null,"abstract":"<span> <h3>Abstract</h3> <p>The diffusive logistic model of information dissemination in a social network is considered in the form of a one-dimensional nonstationary parabolic equation. The problem of parametric identification is posed as an extremum problem for searching the parameter in the form of a spatially distributed social network capacity. Gradient means of optimization are applied. The results demonstrate uniform convergence to an exact solution in the approach with a controllable descent direction.</p> </span>","PeriodicalId":501582,"journal":{"name":"Moscow University Computational Mathematics and Cybernetics","volume":"68 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying the Capacity of a Social Network\",\"authors\":\"\",\"doi\":\"10.3103/s0278641924010084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<span> <h3>Abstract</h3> <p>The diffusive logistic model of information dissemination in a social network is considered in the form of a one-dimensional nonstationary parabolic equation. The problem of parametric identification is posed as an extremum problem for searching the parameter in the form of a spatially distributed social network capacity. Gradient means of optimization are applied. The results demonstrate uniform convergence to an exact solution in the approach with a controllable descent direction.</p> </span>\",\"PeriodicalId\":501582,\"journal\":{\"name\":\"Moscow University Computational Mathematics and Cybernetics\",\"volume\":\"68 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Moscow University Computational Mathematics and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3103/s0278641924010084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Moscow University Computational Mathematics and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s0278641924010084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The diffusive logistic model of information dissemination in a social network is considered in the form of a one-dimensional nonstationary parabolic equation. The problem of parametric identification is posed as an extremum problem for searching the parameter in the form of a spatially distributed social network capacity. Gradient means of optimization are applied. The results demonstrate uniform convergence to an exact solution in the approach with a controllable descent direction.