Social Network Analysis Using Author Co-Citation Data

This study examines the social network of scholars in the field of Communication by using author co-citation data. A matrix containing the number of co-cited documents between pairs of authors is created for social network analysis of scholars who are on the editorial board of Journal of Communication, and the networked map of the scholars is used to visualize the knowledge structure of the field by identifying groups of authors who are more central than others. Social Science Citation Index (SSCI) is used to collect the author co-citation data, and UCInet is employed for social network analysis as well as network visualization

Network Effects on Scientific Collaborations

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. Methodology/Principal Findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. Conclusions/Significance: Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations. © 2013 Uddin et al.published_or_final_versio

Predicting the impact of academic articles on marketing research: Using machine learning to predict highly cited marketing articles

The citation count of an academic article is of great importance to researchers and readers. Due to the large increase in the publication of academic articles every year, it may be difficult to recognize the articles which are important to the field. This thesis collected data from Scopus with the purpose to analyze how paper, journal, and author related variables performed as drivers of article impact in the marketing field, and how well they could predict highly cited articles five years ahead in time. Social network analysis was used to find centrality metrics, and citation count one year after publication was included as the only time dependent variable. Our results found that citations after one year is a strong driver and predictor for future citations after five years. The analysis of the co-authorship network showed that closeness centrality and betweenness centrality are drivers of future citations in the marketing field, indicating that being close to the core of the network and having brokerage power is important in the field. With the use of machine learning methods, we found that a combination of paper, journal, and author related drivers perform better at predicting highly cited articles after five years, compared to using only one type of driver.nhhma

Visualizing 17 Years of CDIO Influence via Bibliometric Data Analysis

Bibliometric data analysis has gained popularity in recent years as an efficient means of\ua0visualizing multi-dimensional indicators of influence in communities of practice (Youtie &\ua0Shapira, 2008). Such an approach has been used to map emerging fields of research such as\ua0synthetic biology and nanotechnology (Shapira, Kwon, & Youtie, 2017; Youtie & Shapira,\ua02008). Using this approach, one can track citation and social network data over time to develop\ua0a deeper understanding of the influence of the CDIO initiative on engineering education\ua0publications since its inception (i.e., the past 17 years). In this paper, bibliometric data analysis\ua0will be used to examine how publications on the CDIO Initiative have evolved. Visualizations\ua0are presented using an open-source visualization tool, VOSViewer, and used to understand\ua0geographic distribution and co-authorship. A word frequency and co-occurrence analysis has\ua0been used to analyze title and abstract data over the same time period. Geographic author\ua0network analysis reveals continued growth in regional collaborations over the past seventeen\ua0years. Co-authorship by author name reveals a core community of researchers, which has\ua0diverged over time into dispersed collaboration groups. Word co-occurrence analysis of title\ua0and abstract data from Scopus reveals that design-implement and project-based learning\ua0activities have been the central topic of CDIO-related engineering education literature over this\ua0time period. An analysis of the terms “faculty competence” and “learning assessment” indicates\ua0that these topics are comparatively under-served in the literature, representing fertile research\ua0topics for practitioners. The benefit of this research is to provide insight to past development\ua0areas and opportunities for growth in the CDIO Initiative

Visualizing 17 Years of CDIO Influence via Bibliometric Data Analysis

Bibliometric data analysis has gained popularity in recent years as an efficient means of\ua0visualizing multi-dimensional indicators of influence in communities of practice (Youtie &\ua0Shapira, 2008). Such an approach has been used to map emerging fields of research such as\ua0synthetic biology and nanotechnology (Shapira, Kwon, & Youtie, 2017; Youtie & Shapira,\ua02008). Using this approach, one can track citation and social network data over time to develop\ua0a deeper understanding of the influence of the CDIO initiative on engineering education\ua0publications since its inception (i.e., the past 17 years). In this paper, bibliometric data analysis\ua0will be used to examine how publications on the CDIO Initiative have evolved. Visualizations\ua0are presented using an open-source visualization tool, VOSViewer, and used to understand\ua0geographic distribution and co-authorship. A word frequency and co-occurrence analysis has\ua0been used to analyze title and abstract data over the same time period. Geographic author\ua0network analysis reveals continued growth in regional collaborations over the past seventeen\ua0years. Co-authorship by author name reveals a core community of researchers, which has\ua0diverged over time into dispersed collaboration groups. Word co-occurrence analysis of title\ua0and abstract data from Scopus reveals that design-implement and project-based learning\ua0activities have been the central topic of CDIO-related engineering education literature over this\ua0time period. An analysis of the terms “faculty competence” and “learning assessment” indicates\ua0that these topics are comparatively under-served in the literature, representing fertile research\ua0topics for practitioners. The benefit of this research is to provide insight to past development\ua0areas and opportunities for growth in the CDIO Initiative

The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS

An open database of productivity in Vietnam's social sciences and humanities for public use

This study presents a description of an open database on scientific output of Vietnamese researchers in social sciences and humanities, one that corrects for the shortcomings in current research publication databases such as data duplication, slow update, and a substantial cost of doing science. Here, using scientists’ self-reports, open online sources and cross-checking with Scopus database, we introduce a manual system and its semi-automated version of the database on the profiles of 657 Vietnamese researchers in social sciences and humanities who have published in Scopus-indexed journals from 2008 to 2018. The final system also records 973 foreign co-authors, 1,289 papers, and 789 affiliations. The data collection method, highly applicable for other sources, could be replicated in other developing countries while its content be used in cross-section, multivariate, and network data analyses. The open database is expected to help Vietnam revamp its research capacity and meet the public demand for greater transparency in science management

On Fractional Approach to Analysis of Linked Networks

In this paper, we present the outer product decomposition of a product of compatible linked networks. It provides a foundation for the fractional approach in network analysis. We discuss the standard and Newman's normalization of networks. We propose some alternatives for fractional bibliographic coupling measures