Discovering cross-topic collaborations among researchers by exploiting weighted association rules

Luca Cagliero (), Paolo Garza (), Mohammad Reza Kavoosifar () and Elena Baralis ()
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Luca Cagliero: Politecnico di Torino
Paolo Garza: Politecnico di Torino
Mohammad Reza Kavoosifar: Politecnico di Torino
Elena Baralis: Politecnico di Torino

Scientometrics, 2018, vol. 116, issue 2, No 32, 1273-1301

Abstract: Abstract Identifying the most relevant scientific publications on a given topic is a well-known research problem. The Author-Topic Model (ATM) is a generative model that represents the relationships between research topics and publication authors. It allows us to identify the most influential authors on a particular topic. However, since most research works are co-authored by many researchers the information provided by ATM can be complemented by the study of the most fruitful collaborations among multiple authors. This paper addresses the discovery of research collaborations among multiple authors on single or multiple topics. Specifically, it exploits an exploratory data mining technique, i.e., weighted association rule mining, to analyze publication data and to discover correlations between ATM topics and combinations of authors. The mined rules characterize groups of researchers with fairly high scientific productivity by indicating (1) the research topics covered by their most cited publications and the relevance of their scientific production separately for each topic, (2) the nature of the collaboration (topic-specific or cross-topic), (3) the name of the external authors who have (occasionally) collaborated with the group either on a specific topic or on multiple topics, and (4) the underlying correlations between the addressed topics. The applicability of the proposed approach was validated on real data acquired from the Online Mendelian Inheritance in Man catalog of genetic disorders and from the PubMed digital library. The results confirm the effectiveness of the proposed strategy.

Keywords: Author Topic Model; Weighted association rule mining; Data mining; Knowledge discovery (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-018-2737-3

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