用中心性方法分析计算机网络中的合著者

Tanveer Ahmed, Adeel Ahmed, Mubashir Ali, M. Kamran
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引用次数: 7

摘要

近年来,社交网络上的分类传播引起了研究者的关注。大量分享意见、想法、经验和专业知识凸显了通过社交网络进行交流。社交网络已经成为分享这些价值观的蓬勃发展的网络。识别和分析这些通信趋势对于使用社区检测来检测社交网络中人们之间的模式具有重要意义。人们提出了不同的方法来检测社交网络中的社区。合作作者网络也成为许多研究者关注的焦点。特定领域的共同作者社区检测是一个不断发展的领域,它与其他社区检测识别模式一起出现。在本文中,我们使用常见的中心性度量对计算机网络领域的合作网络进行了分析。研究结果表明,本研究发现特定领域的共同作者在计算机网络领域的知识共享和协作方面做得最好。
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Analysis of co-authorship in computer networks using centrality measures
Assorted communication on social networks attracted researcher's attention in recent time. Massive sharing of opinions, ideas, experiences and expertise highlight communication through social networks. Social networks have becoming a flourishing network for sharing such values. Identifying and analyzing these communication trends have gained importance for detecting patterns among peoples in social networks using community detection. Different methods have been proposed for detecting communities in social networks. Co-authorship network is also becoming central point of attention to many researchers. Domain specific co-authorship community detection is evolving area that is emerging beside other community detection identification patterns. In this paper, we performed analysis on coauthorship network in the field of computer networks using common centrality measures. Finding reveals that this study finds domain specific co-authors that are best in their knowledge sharing and collaboration in the field of computer networks.
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