A Learning Based Influence Maximization across Multiple Social Networks

Social networks play a significant role in spreading data in individuals' day-to-day existence. In viral advertising, organizations desire to communicate their items by utilizing the network organization and qualities of influence propagation. In particular, they need to give items at no cost to the chosen clients (seed nodes), permit them to promote them throughout the network and maximize the acquisition. There should be a spreading plan of the free-of-charge items with the objective of the organizations to choose the ideal seed set to boost the influence spread. This issue is known as influence maximization (IM) and has a broad scope viz., suggestion frameworks, link prediction, and data diffusion. In this paper, we worked on finding a connection between the model execution and the size of the chart and finding the seed hub. Impact Maximization (IM) is a principal issue to recognize a tiny arrangement of people which contain maximal impact increase within the community system.