Finding and evaluating community structure in networks

TL;DR

This paper introduces iterative algorithms that detect community structures in networks by removing edges based on betweenness measures, with a new metric to evaluate community strength, applicable to both synthetic and real-world data.

Contribution

The paper presents a novel set of algorithms for community detection that iteratively remove edges using recalculated betweenness measures and introduces an objective metric for community strength.

Findings

Algorithms effectively identify communities in synthetic networks.

Algorithms reveal meaningful structures in real-world networks.

Proposed metric aids in determining optimal number of communities.

Abstract

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.