Document- and Keyword-based Author Co-citation Analysis

Abstract

In the field of scientometrics, the principal purpose for author co-citation analysis (ACA) is to map knowledge domains by quantifying the relationship between co-cited author pairs. However, traditional ACA has been criticized since its input is insufficiently informative by simply counting authors’ co-citation frequencies. To address this issue, this paper introduces a new method that reconstructs the raw co-citation matrices by regarding document unit counts and keywords of references, named as Document- and Keyword-Based Author Co-Citation Analysis (DKACA). Based on the traditional ACA, DKACA counted co-citation pairs by document units instead of authors from the global network perspective. Moreover, by incorporating the information of keywords from cited papers, DKACA captured their semantic similarity between co-cited papers. In the method validation part, we implemented network visualization and MDS measurement to evaluate the effectiveness of DKACA. Results suggest that the proposed DKACA method not only reveals more insights that are previously unknown but also improves the performance and accuracy of knowledge domain mapping, representing a new basis for further studies.

Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes

“…We defined the exploration as exploring behavior level, while the exploitation as publishing behavior level. In author-keyword networks (Wang et al 2018), researchers who have slightly changed interest or focusing on fusion topics can change the network models. However in our proposed model, the change issue is controllable, since the keywords are treated as topics in a broader context.…”

“…Due to the need for tracking trends in academic articles, one-mode network of articles or keywords derived from two-mode network of article-keyword was constructed to understand key points of articles (Li et al 2016). The article-keyword network was also used in particular network type of author-article-keyword to quantify author relationships for citation analysis (Wang et al 2018). By incorporating keyword information, the analysis revealed more insights of knowledge domain mapping for authors.…”

Apple Leaf Disease Recognition with Segmentation Research Trend: A Systematic Analysis Using VoSViewer

“…A bibliometric technique called co-citation reference analysis looks at how often two or more documents are cited in the same reference list of a third publication. The fundamental tenet of co-citation analysis is that two publications are probably connected if they are often referenced together [53] Just 37 of the 12044 cited references in this study met the required amount of citations, which is based on a cited reference having a minimum of 5. But once more, the results will be compared 27 after the study data has been manually vetted shown in figure 7.…”

Modeling the co-citation dependence on semantic layers of co-cited documents

“…More recent studies have brought about improvements in the results of co-citation analysis using new ideas and techniques, namely, semantic similarity at different proximity levels (Sanguri et al ., 2020; Colavizza et al , 2018; Hsiao and Chen, 2017), probability of co-citation (Rodriguez-Prieto et al , 2019) and reconstructing raw co-citation matrices based on document unit counts and keywords of references (Wang et al ., 2018).…”