DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning

Big Data and artificial intelligence are used to transform businesses. Social networking sites have given a new dimension online data. media platforms help gather massive amounts of data reach wide variety customers using influence maximization technique for innovative ideas, products services. This paper aims develop deep learning method that can identify the influential users in network. combines various aspects user into single graph. In social network, most is trusted user. These significant viral marketing as seeds other The proposed both topical topological network collaborative filtering. DeepWalk based Influence Maximization (DWIM). was able find k nodes with computable time algorithm. experiments performed assess algorithm, centrality measures compare results. results reveal its performance time. DWIM users, which helps marketing, outlier detection, recommendations different After applying methodology, set seed gives maximum measured respect an increased