Network Generation Model Based on Evolution Dynamics To Generate Benchmark Graphs
Network generation models provide an understanding of the dynamics behind the formation and evolution of different networks including social networks, technological networks and biological networks. Two important applications of these models are to study the evolution dynamics of network formation and to generate benchmark networks with known community structures. Research has been conducted in both these directions relatively independent of the other application area. This creates a disjunct between real world networks and the networks generated to study community detection algorithms. In this paper, we propose to study both these application areas together i.e. introduce a network generation model based on evolution dynamics of real world networks and generate networks with community structures that can be used as benchmark graphs to study community detection algorithms. The generated networks possess tunable modular structures which can be used to generate networks with known community structures. We study the behaviour of different community detection algorithms based on the proposed model and compare it with other models to generate benchmark graphs. Results suggest that the networks generated using the proposed model present tougher challenges for community detection algorithms due to the topological structure introduced by evolution dynamics.
ArXiv