We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups.Expand

The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years.Expand

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems.Expand

In this article we have examined the problem of detecting community structure in networks, which is framed as an opti- mization task in which one searches for the maximal value of the quantity known as modularity over possible divisions of a network.Expand

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large… Expand

We describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.Expand

When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf’s law or the… Expand

We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach… Expand