Modelling Social Networks Using Modified Hopfield Neural Network and Identify Leadership Traits

This paper aims at studying human behaviour in a group using centrality measures applying Modified Hopfield Neural Network, and analyse the change in group dynamics when chronological event sequences are altered. Further, to understand the personality traits unique to leaders and how to influence people with the right marketing strategy. The simulation considers the intragroup dynamics of centrality where every individual is considered to have a unique personality. Individuals are represented as a node, and the effect of the change in intragroup dynamics are simulated using modified Hopfield Neural Network and Hebb's rule. Our results show that every chronological change in the events can affect a groups opinion and influence the choice of leaders. Also, that the leaders (node with the highest centrality measures) have unique personality trait which comprises a rare combination of adaptability α, retention ρ, and forgetfulness γ.