Detecting communities in large networks

TL;DR

This paper introduces a spectral algorithm for detecting community structures in large, weighted, and directed networks, demonstrating effectiveness on social and psychological data.

Contribution

It presents a novel spectral method capable of handling weighted and directed links, suitable for large-scale complex networks analysis.

Findings

Successfully detects communities in large networks

Effectively clusters words in psychological data

Uncovers mental association patterns

Abstract

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and links orientations. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable to the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words and in uncovering mental association patterns.