Year: 2007
Type: preprint
Abstract: Community detection is an important research topic in complex networks. We present the employment of a genetic algorithm to detect communities in complex networks which is based on optimizing network modularity. It does not need any prior knowledge about the number of communities. Its performance is tested on two real life networks with known community structures and a set of synthetic networks. As the performance measure an information theoretical metric variation of information is used. The results are promising and in some cases better than previously reported studies.
Source: arXiv (Cornell University)
Institution: Boğaziçi University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1668065109