What makes us a community: structure, correlations, and success in scientific world

TL;DR

This paper analyzes the structure of scientific communities using statistical analysis of publication data, revealing insights into community interactions and potential applications for ranking and review processes.

Contribution

It introduces a simple statistical approach based on publication metadata to understand community structure and interactions, with implications for community management.

Findings

Community structure can be inferred from publication metadata.

Semantic content analysis aligns with social interaction patterns.

Potential for improving ranking and reviewer selection processes.

Abstract

We explore the statistical structure of scientific community based on multivariate analysis of publication (or other identifiable metrics) distribution in the author space. Here, we define community based on keywords, i.e. projecting semantic content of the documents on predefined meanings; however, more complex approaches based on semantic clustering of publications are possible. Remarkably, this simple statistical analysis of publication metadata allows understanding of internal interactions with community in general agreement with experience acquired over decades of social interaction within it. We further discuss potential applications of this approach for ranking within the community, reviewer selection, and optimization of community output.