Abstract
With the development of network models, the importance of community structure had caught much attention. How can the community size and the community distance affect the network structure remains unexplored. Therefore, in this paper, the MoncSid-N and the MoncSid-E are proposed in response to the issue The community size and distance preferences are introduced in these two models. The networks generated by the MoncSid-N show a better similarity to the real-world networks. The network metrics, including average degree, distribution of node degree, and distribution of community size, are used to analyze the performance of the MoncSid-N. Meanwhile, the MoncSid-E solves the problems of the evolution of large-scale networks. A parallel implementation by Pregel of the MoncSid-E is proposed. It is shown that the network with millions of nodes can be generated by the MoncSid-E efficiently. Based on the plenty of simulations and the comparison of real-world networks, the performances of the MoncSid-N and the MoncSid-E are testified. (C) 2022 Elsevier B.V. All rights reserved.
NSTL
Elsevier