A Weighted Multi-Local-World Network Evolving Model and Its Application in Software Network Modeling

The phenomenon of local worlds (also known as communities) exists in numerous real-life networks, for example, computer networks and social networks. We proposed the Weighted Multi-Local-World (WMLW) network evolving model, taking into account the dense links between nodes in a local world, the sparse links between nodes from different local worlds, and the different importance between intra-local-world links and inter-local-world links. On topology evolving, new links between existing local worlds and new local worlds are added to the network, while new nodes and links are added to existing local worlds. On weighting mechanism, weight of links in a local world and weight of links between different local worlds are endowed different meanings. It is theoretically proven that the strength distribution of the generated network by the WMLW model yields to a power-law distribution. Simulations show the correctness of the theoretical results. Meanwhile, the degree distribution also follows a power-law distribution. Analysis and simulation results show that the proposed WMLW model can be used to model the evolution of class diagrams of software systems.