Hidden Link Prediction in Stochastic Social Networks

Babita Pandey

ISBN 10: 152259096X / ISBN 13: 9781522590965

Published by Information Science Reference, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9781522590965

Bibliographic Details

• Title: Hidden Link Prediction in Stochastic Social Networks
• Publisher: Information Science Reference
• Publication Date: 2019
• Language: English
• ISBN 10: 152259096X
• ISBN 13: 9781522590965
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 21D millimeters 260 millimeters width by 183 millimeters height by 21 millimeters depth
• Weight: 777 grams

About this title

Synopsis

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.