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

Q4 Environmental Science
Adeel Ahmed, Tanveer Ahmed
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引用次数: 0

摘要

社区检测是社交网络中的一个基本问题。这些网络基于链接分析和强连接强度来检测社区,但不能反映来自不同研究领域的作者。为了解决社区检测问题,我们对“使用模块化和中心性度量分析合作网络中的协作模式”进行了研究。本分析研究采用模块化特征与中心性测度相结合的方法,有效地检测了计算机网络与数据库系统领域中不同作者、不同兴趣、不同研究合作的社区。数据集实验表明,该方法可以更好地从特定领域中发现与其他合著者合作程度较高的最佳作者,并将信息呈现给对相关作者社区有相对兴趣的研究人员。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Iranian Journal of Botany
Iranian Journal of Botany Environmental Science-Ecology
CiteScore
0.80
自引率
0.00%
发文量
0
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