Sul-Ah Ahn and Youngim Jung * Korea Institute of Science and Technology Information Daejeon, Republic of Korea { snowy; * Corresponding Author: acorn High Performance Computing Research Activity: Co- authorship Network Analysis 1

ABSTRACT 1.ABSTRACT 3 3 The research activities of the high-performance computing (HPC) researchers are analyzed using scientometric approaches Purpose ▶ Providing HPC researchers and policy planners with useful scientometric results for an assessment of research activities. Method ▶ Co-authorship network analysis of journal articles in the HPC field Data ▶ Journal articles of the Scopus database from Elsevier covering the time period of Result ▶ Co-authorship network analysis for 50 top-authors and their coauthors in HPC ▶ Description of some features of the co-authorship network in relation to the author rank. Keywords ▶ Research Activity, High Performance Computing (HPC), Co-authorship Network Analysis

INTRODUCTION (1/2) 2.INTRODUCTION 4 4 Purpose of scientometrics ▶ Mapping of the literary production in a given field ▶ Determination of the structure of a field of study Motives ▶ Scientometric analysis in various R&D fields conducted during decades. ▶ Scientometric analysis in HPC and related fields: very rare ▶ HPC is an emerging field of computational research at the intersection of science and computation [1]. Significance of Our Work ▶ The 1 st performed work about the co-authorship analysis on journal publication data in HPC field

INTRODUCTION (2/2) 2.INTRODUCTION 5 5 Previous Work about Co-authorship analysis ▶ 1st Introduction of the concept, “Scientific Collaboration Network” (2001, Newaman_PNAS). Networks of scientists in which two scientists are considered connected if they have coauthored a paper together. The affiliation networks of scientist/co- authorship network (2003, Newman_PRE). ▶ Co-authorship: implication of a level of scientific collaboration (2004, Newman_PNAS) Fig. 1. ▶ Usefulness of the co-authorship network analysis Powerful tool in order to plan strategically research, development and capacity building programs on a specific research field (2009, Morel_PLOS). ▶ Application of citation networks and network analysis of co-authorship Scientific collaboration and endorsement (2011, Ding_J.Informetrics)

Data Source ▶ Journal articles of the Scopus database from Elsevier covering the time period of ▶ Author rank in the high performance computing field by the number of papers published during ten years ▶ Search terms: SUPERCOMPUT*, HIGH PERFORMANCE COMPUT*, HIGH THROUGHPUT COMPUT* Refinement of source data ▶ First names of the authors are abbreviated in Scopus database ▶ Authors who have multiple affiliations are duplicated in the Scopus database => Authors have been identified along with their affiliations in prior to analysis Data 3. Data and Method

Co-authorship network analysis ▶ For revealing collaboration between scholars in HPC ▶ Nodes represent the authors ▶ Edge shows that linked authors Pathfinder network ▶ Pathfinder network using symmetric square matrix whose elements are correlation value(cosine coefficient) between authors has been generated ▶ Pathfinder network algorithm is helpful to describe the structure of the network, because it simplifies the complex network in matrix representation Methodology 3. Data and Method

Number of articles and authors in ▶ Total number of articles: 60,053 articles ▶ Number of articles whose corresponding author is identified: 58,507 articles ▶ Number of authors : 147,681 scholars ▶ Number of authors who have authored more than once: 49,023 (33.2%) ▶ Only 1,965 scholars in HPC have involved in authoring more than 10 articles Statistics of Data 4. Results and Discussions HPC, Growing scientific field

Author Ranks in HPC for last 10 years Author Ranks 4. Results and Discussions Oak Ridge National Laboratory Johns Hopkins University Small number of publications No dominant scholar in HPC yet

Co-authoring rank calculated by cosine coefficient ▶ Relations between top-50 authors and their coauthors have been analyzed. Co-authorship Network in HPC 4. Results and Discussions

Co-authorship Network PFNet 4. Results and Discussions Asian scholars in more active co-authoring Small number of articles published in HPC may have affected on remarkable co- authorship of authors whose number of publications are less

Summary 5.Summary 12 Co-authorship network analysis of J articles to evaluate the research activities of researchers for high-performance computing (HPC) Data ▶ Journal articles of the Scopus database from Elsevier covering during Results ▶ Extraction of Author rank in the HPC field by the number of publication during 10 years ▶ Calculation of he weight of co-authorship ▶ Drawing of the co-authorship network for 50 top-authors and their coauthors Discussions ▶ High ranked authors by publication are not also strong in co-authorship among 50 top-authors and their coauthors. ▶ Some authors, not much high ranked authors by publication, show relatively more strong correlation for the co-authorship. Future Work ▶ To provide significant scientometric results for an evaluation of research activities to HPC researchers and policy planners, co-authorship network analysis or citation network for more than 50 top-authors in the HPC field are suggested to be done.