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.
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