Algorithms for generating large-scale clustered random graphs

Real-world networks are often compared to random graphs to assess whether their topological structure could be a result of random processes. However, a simple random graph in large scale often lacks social structure beyond the dyadic level. As a result we need to generate clustered random graph to compare the local structure at higher network levels. In this paper a generalized version of Gleeson's algorithm G(V-s, V-T, E-s, E-T, S, T) is advanced to generate a clustered random graph in large-scale which persists the number of vertices | V|, the number of edges |E| and the global clustering coefficient C-A as in the real network and it works successfully for nine large-scale networks. Our new algorithm also has advantages in randomness evaluation and computation efficiency when compared with the existing algorithms.