Review of a proposed methodology for bibliometric and visualization analyses for organizations: application to the collaboration economy

This paper presents the bibliometric and visualization method applied to a dataset of 729 documents published in the collaborative economy research field. Four steps are described in details: (1) the delimitation of the field of study; (2) the selection of databases, keywords, and search criteria; (3) the extraction, cleaning, and formatting; and finally (4) the co-citation analysis and visualization. The method validation section shows the results obtained by applying our methodological procedure to an author network analysis as well as a source title network analysis. This study is unique which presents a co-citation analysis coupled with a network visualization applied to the rapidly growing research area of the collaborative economy as a whole and not only of the collaborative tourism and hospitality research, as has been previously. The originality of this method lies firstly in the fact that the data were extracted from two databases (Scopus and Web of Science) instead of one as is commonly done in analytic studies. Secondly, VOSviewer was our main analytical tool performing the co-citation analysis and the network visualizations

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

Australasian Journal of Philosophy 1947–2016: a retrospective using citation and social network analyses

In anticipation of the journal’s centenary in 2027 this paper provides a citation network analysis of all available citation and publication data of the Australasian Journal of Philosophy (1923–2017). A total of 2,353 academic articles containing 21,772 references were collated and analyzed. This includes 175 articles that contained author-submitted keywords, 415 publisher-tagged keywords and 519 articles that had abstracts. Results initially focused on finding the most published authors, most cited articles and most cited authors within the journal, followed by most discussed topics and emerging patterns using keywords and abstracts. The analysis then proceeded to apply social network analysis using Kumu© – a visualization platform for mapping systems and relationships using large datasets. Analysis reveals topic clusters both unique to the journal, and inclusive of the journal’s history. Results from this analysis reaffirm the journal’s continuing focus on topics in traditional analytic philosophy such as morality, epistemology and knowledge, whilst also featuring topics associated with logic and paradox. This paper presents a new approach to analysing and understanding the historic and emerging topics of interest to the journal, and its readership. This has never previously been done for single philosophy journal. This is historically important given the journal’s forthcoming centenary

The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.Comment: 33 pages, 11 figures, 10 tables. To appear in the Journal of the American Society for Information Science and Technolog

Visualization and analysis of SCImago Journal & Country Rank structure via journal clustering

Purpose: The purpose of this paper is to visualize the structure of SCImago Journal & Country Rank (SJR) coverage of the extensive citation network of Scopus journals, examining this bibliometric portal through an alternative approach, applying clustering and visualization techniques to a combination of citation-based links. Design/methodology/approach:Three SJR journal-journal networks containing direct citation, co-citation and bibliographic coupling links are built. The three networks were then combined into a new one by summing up their values, which were later normalized through geo-normalization measure. Finally, the VOS clustering algorithm was executed and the journal clusters obtained were labeled using original SJR category tags and significant words from journal titles. Findings: The resultant scientogram displays the SJR structure through a set of communities equivalent to SJR categories that represent the subject contents of the journals they cover. A higher level of aggregation by areas provides a broad view of the SJR structure, facilitating its analysis and visualization at the same time. Originality/value: This is the first study using Persson’s combination of most popular citation-based links (direct citation, co-citation and bibliographic coupling) in order to develop a scientogram based on Scopus journals from SJR. The integration of the three measures along with performance of the VOS community detection algorithm gave a balanced set of clusters. The resulting scientogram is useful for assessing and validating previous classifications as well as for information retrieval and domain analysis.Peer reviewe