Composing Scientific Collaborations Based on Scholars' Rank in Hypergraph

被引:0
|
作者
Ghasemian, Fahimeh [1 ]
Zamanifar, Kamran [1 ]
Ghasem-Aghaee, Nasser [1 ]
机构
[1] Univ Isfahan, Fac Comp Engn, Dept Software Engn, Esfahan, Iran
关键词
Scientific collaboration; Collaboration network; Hypergraph; Jaccard similarity; Team formation algorithm; TEAM SCIENCE;
D O I
10.1007/s10796-017-9773-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Finding the right scholars for collaboration is crucial for scientific progress. In this study, a novel algorithm is proposed to find the successful team configurations for scientific collaboration in the presence of the collaboration network of scholars. In this algorithm, the collaboration network is exploited to estimate the trust level among team members and the skill level of the scholars, while a hypergraph is used to model the relations. Also, our algorithm improves the search process by directing it to the promising regions, where the probability of finding the successful teams is high. A comparison with other algorithms is done to evaluate the proposed algorithm, using the similarity to successful collaborations. Our findings show that this algorithm achieves a significantly higher performance, compared to the other algorithms.
引用
收藏
页码:687 / 702
页数:16
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