Return to basics: Clustering of scientific literature using structural information. Issue 4 (November 2020) - Yun, Jinhyuk; Ahn, Sejung; Lee, June Young
Highlights: We propose a novel approach to emulate the clustering results of co-citation (CC) and bibliographic coupling (BC). The proposed method simply replicates a node into two distinct nodes: a citing node and a cited node. Citing nodes emulates BC and cited nodes imitates CC with very low computational loads. The proposed method generally outperformed BC and CC regarding cluster accuracy measured by normalized mutual information. The analysis of intracluster and intercluster geographical proximity suggests that the proposed method retains additional information than BC and CC. Abstract: Scholars frequently employ relatedness measures to estimate the similarity between two different items ( e.g., documents, authors, and institutes). Such relatedness measures are commonly based on overlapping references ( i.e., bibliographic coupling) or citations ( i.e., co-citation) and can then be used with cluster analysis to find boundaries between research fields. Unfortunately, calculating a relatedness measure is challenging, especially for a large number of items, because the computational complexity is greater than linear. We propose an alternative method for identifying research fronts that uses direct citation inspired by relatedness measures. Our novel approach simply replicates a node into two distinct nodes: a citing node and cited node. We then apply typical clustering methods to the modified network. Clusters of citing nodes should emulate those from the bibliographic coupling relatedness network, while clusters of cited nodes should act like those from the co-citation relatedness network. In validation tests, our proposed method demonstrated high levels of similarity with conventional relatedness-based methods. We also found that the clustering results of the proposed method outperformed those of conventional relatedness-based measures regarding similarity with natural language processing-based classification.