Maps of random walks on complex networks reveal community structure

TL;DR

This paper introduces an information theoretic method to uncover community structures in weighted, directed networks, exemplified by mapping scientific journal citation patterns to reveal organizational insights.

Contribution

The paper presents a novel approach that decomposes networks into modules by compressing information flow descriptions, providing clearer community maps in complex systems.

Findings

Revealed a multicentric organization of scientific fields

Identified bidirectional information flow in core sciences

Discovered directional citation patterns from applied to basic sciences

Abstract

To comprehend the multipartite organization of large-scale biological and social systems, we introduce a new information theoretic approach that reveals community structure in weighted and directed networks. The method decomposes a network into modules by optimally compressing a description of information flows on the network. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of more than 6000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network -- including physics, chemistry, molecular biology, and medicine -- information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.