Generating synthetic networks that simulate real-world networks is an area considered as being a hot topic of research. Indeed, access to real-world networks is not available all the times. Thus, synthetic networks are very much required for the development of network research science. In this paper, we propose a new model designed to generate synthetic networks that adhere to well-known characteristics of real-world networks such as node degree distribution, the clustering coefficient, and k-core structure distributions of real-world networks. It takes the target cluster coefficient and the degree sequences of the real-world network as inputs; and generates a synthetic network witha cluster coefficient close to the target cluster coefficient. The key advantage of our proposed model is its ability to generate synthetic networks with semi-accurate cluster coefficients by generating exactly the required number of triangles. The experimental results show that our proposed model generates networks with characteristics close to characteristics approximating those of real-world networks.

synthetic networks, clustering coefficient, real-world networks, semi-accurate.