Information Science Reference Hidden Link Prediction in Stochastic Social Networks - IGI Global

Understanding the evolutionary theory of computing requires a deep dive into how social networks grow and change. Hidden Link Prediction in Stochastic Social Networks provides a technical look at the complexities involved in identifying missing connections within evolving digital structures. As social networks grow stochastically, researchers face significant hurdles in accurately representing graphs and distinguishing between spurious links and actual missing links. This book addresses these specific challenges by examining the selection of link prediction techniques based on network features and the identification of various network types. It concentrates on the primary methods and approaches used to predict hidden links within stochastic environments. This resource is designed for those needing to navigate the difficulties of graph representation and the nuances of network evolution. By studying these advanced techniques, readers can better understand the mechanics behind social network growth and the computational theories that drive them.