Guest Editorial: Big Scholar Data Discovery and Collaboration

THE last part of the special issue includes two papers—an interesting empirical study using the very large scholarly dataset described in the solicitation of this special issue, and a survey paper serving as an outlook of the field. In the paper “The Habits of Highly Effective Researchers: An Empirical Study” by Datta, Basuchowdhuri, Acharya and Majumder, the authors attempted to discern some common patterns of the highly influential researchers. The authors first introduced an algorithm for selecting the set of influential researchers—nodes that can spread information effectively in a network. Using the large-scale citation and co-authorship networks available from the AMiner dataset, the authors extracted influential researchers through the proposed algorithm and examined several factors related to their success, such as diversity of the paper content and the collaboration activity. Interesting observations on the influential researchers in five computing domains were presented, and the authors also provided insightful discussion about the implication and limitation of the analysis. The paper “Big Scholarly Data: A Survey” by Xia, Wang, Bekele and Liu, presented a comprehensive review of the state of the research surrounding the “Big Scholarly Data.” The authors summarized the current research from three perspectives—management, analysis methods, and applications such as scientific impact evaluation and recommendation. The paper also raised key open issues and discussed future directions. This paper can be a great reference guide for researchers who are new to these topics or for those who wish to better understand this emerging and interdisciplinary field. We felt the paper can serve as a springboard that stimulates further research innovation and discovery and thus greatly contributes to the “Big Scholar Data” research community.