Modeling Clustered Scale free Networks by Applying Various Preferential Attachment Patterns
Preferential attachment phenomenon is a key factor providing scale free behavior in complex networks. In this study, we introduced various preferential attachment patterns applied in a growing Barabasi Albert network, denoted by a factor a. We first generated networks under constant preferential attachment levels from 0 to 2, where 1 stands for linear preferential attachment. Then we performed network simulations under uniformly distributed random a condition, within the interval [0,2] Although mean a is 1 for this setup, generated networks displayed greater clustering together with lower modularity and separation values compared to the setup with a=1. We also performed similar network generation procedures with various distribution functions applied for a, each resulting random levels of preferential attachment. We achieved networks with power law consistent degree distributions with g coefficients between 2 and 3, together with improved clustering coefficients up to ~0.3. As a result, scale free network topologies featuring greater clustering levels compared to pure Barabasi Albert model are achieved.