Center-Periphery Structure in Communities: Extracellular Vesicles

TL;DR

This paper introduces a new pipeline for detecting scientific publication communities based on citation networks, specifically applied to extracellular vesicle research, and compares it with existing algorithms.

Contribution

The authors develop a modular community detection pipeline based on the k-core algorithm and evaluate it against the Leiden algorithm on a large citation network.

Findings

The pipeline effectively identifies publication communities in large citation networks.

Comparison shows the pipeline's performance is competitive with Leiden algorithm.

Application to extracellular vesicle publications reveals meaningful research communities.

Abstract

Clustering and community detection in networks are of broad interest and have been the subject of extensive research that spans several fields. We are interested in the relatively narrow question of detecting communities of scientific publications that are linked by citations. These publication communities can be used to identify scientists with shared interests who form communities of researchers. Building on the well-known k-core algorithm, we have developed a modular pipeline to find publication communities. We compare our approach to communities discovered by the widely used Leiden algorithm for community finding. Using a quantitative and qualitative approach, we evaluate community finding results on a citation network consisting of over 14 million publications relevant to the field of extracellular vesicles.