Hidden Link Prediction in Stochastic Social Networks

Link prediction is required to understand the evolutionary theory of computing for different social networks. However the stochastic growth of the social network leads to various challenges in identifying hidden links such as representation of graph distinction between spurious and missing links selection of link prediction techniques comprised of network features and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques matrix factorization reinforcement models and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning agent-driven AI techniques and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised unsupervised and recommendation-driven learning systems. It is intended for use by data scientists technology developers professionals students and researchers.