The Community Structure of Econophysicist Collaboration Networks

TL;DR

This paper analyzes the community structure of econophysicist collaboration networks using hierarchical clustering and Girvan-Newman algorithms, highlighting the impact of edge weights on community detection results.

Contribution

It introduces a comparison method for community detection results with different edge weights and provides insights into the community structure of econophysics collaboration networks.

Findings

Edge weights significantly influence community detection outcomes.

Hierarchical clustering and Girvan-Newman algorithms reveal distinct community structures.

A new function D effectively distinguishes differences between community detection results.

Abstract

This paper uses a database of collaboration recording between Econophysics Scientists to study the community structure of this collaboration network, which with a single type of vertex and a type of undirected, weighted edge. Hierarchical clustering and the algorithm of Girvan and Newman are presented to analyze the data. And it emphasizes the influence of the weight to results of communities by comparing the different results obtained in different weights. A function D is proposed to distinguish the difference between above results. At last the paper also gives explanation to the results and discussion about community structure.