Evolving Small-World Networks of the Local World with Tunable Clustering

In this paper, we propose an evolving small-world network model based on a local-world preferential attachment with tunable parameters. The simulation results show our model exhibits the small-world property, the scale-free structure and the disassortative mixing. Moreover, in our model, the clustering coefficient is tunable simply by changing the parameter value. Since two important mechanisms taken into account, changeable local-world size and triad formation, our model has a stronger adaptability and may provide valuable insight into the real-life networks.