Hidden Link Prediction in Stochastic Social Networks

Hidden Link Prediction in Stochastic Social Networks

Babita Pandey (Lovely Professional University, India) and Aditya Khamparia (Lovely Professional University, India)
Release Date: May, 2019|Copyright: © 2019 |Pages: 281
ISBN13: 9781522590965|ISBN10: 152259096X|EISBN13: 9781522590972|DOI: 10.4018/978-1-5225-9096-5


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.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence
  • E-Business
  • E-Commerce
  • Information Security
  • Machine Learning
  • Mobile Technology
  • Network Analysis
  • Secure Systems
  • Security Management
  • Social Media

Table of Contents and List of Contributors

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