Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks

▶ This study applied the combination of a topic modeling algorithm and a path-finding algorithm to mine research topics of scientists based on their publications and identified their semantic associations based on coauthorship networks and author citation networks. ▶ This paper was able to address the collaboration patterns and citation patters at the topic level rather than at the domain/disciplinary level. Scientific collaboration and endorsement are well-established research topics which utilize three kinds of methods: survey/questionnaire, bibliometrics, and complex network analysis. This paper combines topic modeling and path-finding algorithms to determine whether productive authors tend to collaborate with or cite researchers with the same or different interests, and whether highly cited authors tend to collaborate with or cite each other. Taking information retrieval as a test field, the results show that productive authors tend to directly coauthor with and closely cite colleagues sharing the same research interests; they do not generally collaborate directly with colleagues having different research topics, but instead directly or indirectly cite them; and highly cited authors do not generally coauthor with each other, but closely cite each other.