Dynamic Network Models Made By Multiple Probabilistic Mechanism

As known, no more attentions are put to multiple stochastic network models, we design multiple probabilistic dynamic network models in this article for dealing with a network having two or more different communities with different growing mechanisms and preferential attachment mechanisms. We propose three construction algorithms for producing three types of models after defining operators consisted of graphs and graph operations, and get three spaces generated by three types of models. We introduce partial technique, such as partial multiple preferential attachment mechanism, partial degree distribution etc. in dealing with topological structures of our models. Multiple probabilistic community network models are particular models, but close to social networks. We distribute problems for further research.