Hidden link prediction in stochastic social networks [electronic resource] / Babita Pandey and Aditya Khamparia,editors.
- 其他作者：
- 出版： Hershey, PA : IGI Global 2020.
- 叢書名： Advances in social networking and online communities (ASNOC) book series
- 主題： Computer network architectures. , Online social networks--Data processing. , Webometrics. , Prediction theory. , Intersection theory (Mathematics) , Stochastic analysis.
- ISBN： 9781522590972 (ebk.) 、 9781522590965 (hbk.) 、 9781522590996 (pbk.)
- URL：
點擊此處查看電子書
點擊此處查看電子書
- 一般註："Premier reference source"-- book cover. Includes bibliographical references. 111年度臺灣學術電子書暨資料庫聯盟採購
-
讀者標籤：
- 系統號： 000298871 | 機讀編目格式
館藏資訊
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.
摘要註
"This book examines the foremost techniques of hidden link predictions in stochastic social networks. It deals, principally, with methods and approaches that involve similarity index techniques, matrix factorization, reinforcement models, graph representations and community detections"--
