A Model to Generate Benchmark Network with Community Structure

Abstract

The studies of social networks focus on the structure and components of networks at different levels. To identify the component in a network, researchers have developed various community detection algorithms. To test the quality of community detection results, networks with well-known community structures are used. But, a very few networks are available for this purpose. Researchers have suggested some models that generate artificial networks with the community. However, most of the proposed models are unable to produce benchmark networks similar to the real-world network. We propose a model that generates benchmark networks for the evaluation of community detection algorithms. The proposed model has been compared with well-known LFR Lancichinetti et al. (Phys Rev 78(4):046110, 2008 [14]) and GLFR Le et al. (2017 26th international conference on computer communication and networks (ICCCN). IEEE, pp 1–9, 2017[15]) models. For performance testing, various structural properties have been analyzed, which are followed by real-world networks. The NMI scores achieved by well-known community detection algorithms were also compared. In experimental analysis, we found that networks generated by our model follow essential properties of real-world networks.