  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
($\textit{i.e.}$, bibliographic coupling) or citations ($\textit{i.e.}$,
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 the research front 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 proposed method outperformed those of conventional relatedness-based
measures regarding similarity with natural language processing--based
classification.
