Generalization of bibliographic coupling and co-citation using the node split network

TL;DR

This paper introduces novel methods using personalized PageRank and neural embeddings to accurately estimate intralayer similarities in bibliographic networks, enhancing the understanding of long-range relationships between scientific items.

Contribution

It proposes new techniques for estimating intralayer similarity in node split networks, improving upon previous models by capturing long-range and distant item relationships.

Findings

Proposed measures are strongly correlated with traditional coupling measures.

Methods yield precise similarities even for distant items.

Many high-similarity links are missing in original networks, highlighting the importance of long-range similarities.

Abstract

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.