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  • × author_ss:"Xu, J."
  • × year_i:[2010 TO 2020}
  1. Zhang, C.; Bu, Y.; Ding, Y.; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment (2018) 0.04 
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    Abstract
    Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.
  2. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.04 
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    Abstract
    Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author-Conference-Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h-indices. We find that the collaborators of high-impact researchers tend to study diverse research topics and have diverse h-indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non-authoritative authors carries more weight after PhD graduation.
  3. Xu, J.: Author credit-assignment schemas : a comparison and analysis (2016) 0.03 
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    Abstract
    Credit assignment to multiple authors of a publication is a challenging task owing to the conventions followed within different areas of research. In this study, we present a review of different author credit-assignment schemas, which are designed mainly based on author position and the total number of coauthors on the publication. We implemented, tested, and classified 15 author credit-assignment schemas into 3 types: linear, curve, and "other" assignment schemas. Further investigation and analysis revealed that most of the methods provide reasonable credit-assignment results, even though the credit-assignment distribution approaches are quite different among different types. The evaluation of each schema based on PubMed articles published in 2013 shows that there exist positive correlations among different schemas and that the similarity of credit-assignment distributions can be derived from the similar design principles that stress the number of coauthors or the author position, or consider both. We provide a summary about the features of each credit-assignment schema to facilitate the selection of the appropriate one, depending on the different conditions required to meet diverse needs.

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