Community Based Influencer Node Identification using Hybrid Optimisation Algorithm in Social Networks

In social networking, influence maximisation (IM) refers to identifying influential people who will maximise the adoption of information or products. Hence, in the proposed work, the system offers a novel hybrid optimisation approach for identifying the top influential nodes based on community structure, which improves the diffusion of influence in social networks. In this, the improved wild horse genetic algorithm-based optimisation (IWHGAO) is proposed to detect the community in the social network. The proposed IWHGAO has the balanced intensification and diversification capability that provides the optimal solution for detecting the community, and the fast convergence of the algorithm ensures a minimal execution time. Then, the candidate community and node selection are performed before the seed selection using the proposed hybrid k-shell honey decomposition. Python software is used to implement the suggested model for analysing the performance in terms of influence, execution time, accuracy, spread size, and gNMI value of the proposed method.