摘要：The analysis of scientific collaboration networks has contributed significantly to improving the understanding of the process of collaboration between researchers. Additionally, it has helped to understand how scientific productions by researchers and research groups evolve. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the prevalent methods. This paper proposes a method for identifying collaborations in large scientific databases, namely, ISColl – Identification of Scientific Collaboration. Unlike methods that use techniques such as cross-validation, the proposed method produces satisfactory results with a low computational cost, thus providing an interesting alternative for the modeling and characterization of large scientific collaboration networks.
其他摘要：The analysis of scientific collaboration networks has contributed significantly to improving the understanding of the process of collaboration between researchers. Additionally, it has helped to understand how scientific productions by researchers and research groups evolve. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the prevalent methods. This paper proposes a method for identifying collaborations in large scientific databases, namely, ISColl – Identification of Scientific Collaboration. Unlike methods that use techniques such as cross-validation, the proposed method produces satisfactory results with a low computational cost, thus providing an interesting alternative for the modeling and characterization of large scientific collaboration networks.
关键词：Extraction and data integration; Information Retrieval; Identification of Collaboration
其他关键词：Ciências Sociais Aplicadas; Ciência da Informação;Extraction and data integration; Information Retrieval; Identification of Collaboration
