The art of community detection

TL;DR

This paper discusses the evolution of community detection in complex networks, highlighting a new hierarchical algorithm that uncovers multi-level structures to better understand network organization.

Contribution

It introduces a general framework for community detection and contextualizes a recent hierarchical algorithm within this framework.

Findings

Hierarchical community detection reveals multi-level network structures.

The algorithm uncovers heterogeneities in complex networks.

Framework integration aids future research in network analysis.

Abstract

Networks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.