  Bibliographic coupling (BC) and co-citation (CC) are the two most common
citation-based coupling measures of similarity between scientific items. One
can interpret these measures as second-neighbor relations distinguished by the
direction of the citation: BC is a similarity between two citing items, whereas
CC is that between two cited items. A previous study proposed a two-layer node
split network that can emulate clusters of coupling measures in a
computationally efficient manner; however, the lack of intralayer links makes
it impossible to obtain exact similarities. Here, we propose novel methods to
estimate intralayer similarity on a node split network using personalized
PageRank and neural embedding. We demonstrate that the proposed measures are
strongly correlated with the coupling measures. Moreover, our proposed method
can yield precise similarities between items even if they are distant from each
other. We also show that many links with high similarity are missing in the
original BC/CC network, which suggests that it is essential to consider
long-range similarities. Comparative experiments on global and local edge
sampling suggest that local sampling is stable for both similarities in node
split networks. This analysis offers valuable insights into the process of
searching for significantly related items regarding each coupling measure.
