# The Genetic Programming Collaboration Network and its Communities

## TL;DR

This paper analyzes the structure of the genetic programming research community by examining coauthorship networks, revealing community formations, similarities to other scientific fields, and unique features like high clustering and smaller central components.

## Contribution

It introduces a novel analysis of the GP collaboration network using graph theory, uncovering community structures and differences from other scientific collaboration networks.

## Key findings

- Identification of community structures within GP coauthorship networks
- Discovery of high clustering and smaller central components
- Revealing similarities and subtle differences with other scientific fields

## Abstract

Useful information about scientific collaboration structures and patterns can be inferred from computer databases of published papers. The genetic programming bibliography is the most complete reference of papers on GP\@. In addition to locating publications, it contains coauthor and coeditor relationships from which a more complete picture of the field emerges. We treat these relationships as undirected small world graphs whose study reveals the community structure of the GP collaborative social network. Automatic analysis discovers new communities and highlights new facets of them. The investigation reveals many similarities between GP and coauthorship networks in other scientific fields but also some subtle differences such as a smaller central network component and a high clustering.