Hidden Link Prediction in Stochastic Social Networks

Babita Pandey (u. a.)

ISBN 10: 1522590994 / ISBN 13: 9781522590996

Published by Information Science Reference, 2019

About this Item

Hidden Link Prediction in Stochastic Social Networks | Babita Pandey (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Information Science Reference | EAN 9781522590996 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 119078907

Bibliographic Details

• Title: Hidden Link Prediction in Stochastic Social Networks
• Publisher: Information Science Reference
• Publication Date: 2019
• Language: English
• ISBN 10: 1522590994
• ISBN 13: 9781522590996
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 17D millimeters 254 millimeters width by 178 millimeters height by 17 millimeters depth
• Weight: 583 grams
• Seller catalogs:
  • Bücher

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.