A new approach for detecting scientific specialties from raw cocitation networks

TL;DR

This paper introduces a novel, topology-based algorithm for detecting scientific specialties from cocitation networks, offering an objective, scalable, and interpretable method that aligns well with known scientific structures.

Contribution

It applies a recent community detection algorithm to cocitation networks, avoiding subjective measures and enabling unambiguous identification of scientific specialties.

Findings

Specialties are the smallest coherent researcher groups.

Communities correspond well with known scientific disciplines.

Keywords from titles effectively characterize specialties.

Abstract

We use a technique recently developed by Blondel et al. (2008) in order to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most of the previous methods used to obtain cocitation "clusters", since it avoids the use of similarity measures, relies entirely on the topology of the weighted network and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent "group" of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that, as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks, and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.