Can I read Hidden Link Prediction in Stochastic Social Networks on EtoBox?

Hidden Link Prediction in Stochastic Social Networks by Babita Pandey; Aditya Khamparia; IGI Global is a nonfiction available to read on EtoBox.

What is Hidden Link Prediction in Stochastic Social Networks about?

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.

Who reads Hidden Link Prediction in Stochastic Social Networks?

Author

Babita Pandey; Aditya Khamparia; IGI Global

Publisher

Information Science Reference

Published

2019

Language

EN

ISBN

9781522590965

Category

nonfiction

Subjects

Mathematics, Engineering, Sociology