{"title":"从合作网络的分析评估数据库系统和计算机网络中的研究合作","authors":"Adeel Ahmed, Tanveer Ahmed","doi":"10.33897/fujeas.v1i1.199","DOIUrl":null,"url":null,"abstract":"Community detection is a fundamental problem in social networks. These networks detect communities based on link analysis and strong connection strengths, but cannot reflect Author’s from different research areas. To address the problem of community detection, we have done a study for “Analyzing patterns of collaboration in co-authorship network using Modularity and Centrality Measures”. This analysis study uses combine features of Modularity with centrality measure to effectively detect community of different author’s having different research collaboration with different interests in domain of Computer Networks and Database Systems. Experiment of Dataset shown that this approach is better detect best authors from specific domain having high collaboration with other coauthors and presents information to the researcher’s that have relative interest in relative author’s community.","PeriodicalId":36255,"journal":{"name":"Iranian Journal of Botany","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing Research Collaboration in Database Systems and Computer Networks by Analysis of Coauthorship Network\",\"authors\":\"Adeel Ahmed, Tanveer Ahmed\",\"doi\":\"10.33897/fujeas.v1i1.199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Community detection is a fundamental problem in social networks. These networks detect communities based on link analysis and strong connection strengths, but cannot reflect Author’s from different research areas. To address the problem of community detection, we have done a study for “Analyzing patterns of collaboration in co-authorship network using Modularity and Centrality Measures”. This analysis study uses combine features of Modularity with centrality measure to effectively detect community of different author’s having different research collaboration with different interests in domain of Computer Networks and Database Systems. Experiment of Dataset shown that this approach is better detect best authors from specific domain having high collaboration with other coauthors and presents information to the researcher’s that have relative interest in relative author’s community.\",\"PeriodicalId\":36255,\"journal\":{\"name\":\"Iranian Journal of Botany\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iranian Journal of Botany\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33897/fujeas.v1i1.199\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iranian Journal of Botany","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33897/fujeas.v1i1.199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Environmental Science","Score":null,"Total":0}
Assessing Research Collaboration in Database Systems and Computer Networks by Analysis of Coauthorship Network
Community detection is a fundamental problem in social networks. These networks detect communities based on link analysis and strong connection strengths, but cannot reflect Author’s from different research areas. To address the problem of community detection, we have done a study for “Analyzing patterns of collaboration in co-authorship network using Modularity and Centrality Measures”. This analysis study uses combine features of Modularity with centrality measure to effectively detect community of different author’s having different research collaboration with different interests in domain of Computer Networks and Database Systems. Experiment of Dataset shown that this approach is better detect best authors from specific domain having high collaboration with other coauthors and presents information to the researcher’s that have relative interest in relative author’s community.