  Real world complex networks are scale free and possess meso-scale properties
like core-periphery and community structure. We study evolution of the core
over time in real world networks. This paper proposes evolving models for both
unweighted and weighted scale free networks having local and global
core-periphery as well as community structure. Network evolves using
topological growth, self growth, and weight distribution function. To validate
the correctness of proposed models, we use K-shell and S-shell decomposition
methods. Simulation results show that the generated unweighted networks follow
power law degree distribution with droop head and heavy tail. Similarly,
generated weighted networks follow degree, strength, and edge-weight power law
distributions. We further study other properties of complex networks, such as
clustering coefficient, nearest neighbor degree, and strength degree
correlation.
