An Efficient Evolutionary Approach for Identifying Evolving Groups in Dynamic Social Networks Using Genetic Modeling

Networks could be modeled as graphs, where nodes (or vertices) represent the objects and edges (or links) represent the interactions among these objects. The flexibility of networks to present numerous real world complicated systems, admitting those undergoing dynamic shifts of their structure, is establishing a raising concern in the field of their topological characteristics. The detection of dynamic community structure, that is, the organization of nodes into groups having numerous connections within the identical cluster and comparatively sparse connections among vertices of various communities is a serious problem. Automated dynamic approach is necessary to understand dynamic evolution of communities within the multimode network. In this paper, a generalized framework for dynamic evolution of communities within multimodal network is identified using genetic approach. This framework uses genetics based evolutionary approach for dynamic clustering effects of interacted modes and shows how attributes are taken for updating communities of one mode. The proposed approach evaluates how well the dynamic clusters can be constructed within the information by reducing the temporal cost, which evaluates the distance among two clustering at succeeding time steps. Simulation results signify the benefits of the proposed dynamic approach in both synthetic and the real life networks with the capacity of the multi-objective genetic method to properly determine evolutionary communities with solutions aggressive.