A social network is an online platform, where people communicate and share information with each other. Popular social network features, which make them di erent from traditional communication platforms, are: following a user, re-tweeting a post, liking and commenting on a post etc. Many companies use various social networking platforms extensively as a medium for marketing their products. A xed amount of budget is alloted by the companies to maximize the positive in uence of their product. Every social network consists of a set of users (people) with connections between them. Each user has the potential to extend its in uence across this network. The amount of in uence propagated by some users is larger as compared to others. Companies, given a xed budget, target this subset of users to attain maximum in uence spread. We can model this as an in uence maximization problem. This subset of users then in uence the behavior or choices of other users by methods like word of mouth, actions etc. through an in uence propagation across the network. The aim of this project is to compare di erent known and new proposed algorithms for the in uence maximization problem. We measure the e ciency of each algorithm based on the number of vertices in uenced by the initial set of in uencers. In addition, a comparison of computation time required for each algorithm is done using various synthetic random graphs and real world social network datasets
