MutatedSocioAgentSim (MSAS): semisupervised modelling of multiagent simulation to predict and detect the mutation in a camouflaged social network

Abstract

A social network is a networked structure formed by a set of agents/actors. It describes their interrelationshipsthat facilitate the exchange and flow of resources and information. A camouflaged social network is one such communitythat influences the underlying structure and the profile of the agents, to cause mutation. The proposed MSAM is anovel system that simulates a multiagent network whose community structure is analyzed to identify the critical agentsby studying the mutations caused due to attachment and detachment of agents. The isolation of the tagged agents willdemonstrate disruption of information flow, which leads to the dismantling of the camouflaged community and givingscope for a predictive study about near future reconciliation. The proposed system simulates the 9/11 covert networkbased on the belief matrix and uses the novel density-based link prediction and suite of fragmentation algorithms forpredictive community analysis. MSAM is claimed to be an intelligent system as agents perceive the knowledge fromthe dynamic environment through the belief matrix and further co-evolve as a community upon which semisupervisedmethodologies are used to predict the critical agents causing serious mutation.