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Machine Learning in Social Networks - Embedding Nodes, Edges, Communities, and Graphs (Paperback, 1st ed. 2021)
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Machine Learning in Social Networks - Embedding Nodes, Edges, Communities, and Graphs (Paperback, 1st ed. 2021)
Series: SpringerBriefs in Computational Intelligence
Expected to ship within 10 - 15 working days
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This book deals with network representation learning. It deals with
embedding nodes, edges, subgraphs and graphs. There is a growing
interest in understanding complex systems in different domains
including health, education, agriculture and transportation. Such
complex systems are analyzed by modeling, using networks that are
aptly called complex networks. Networks are becoming ubiquitous as
they can represent many real-world relational data, for instance,
information networks, molecular structures, telecommunication
networks and protein-protein interaction networks. Analysis of
these networks provides advantages in many fields such as
recommendation (recommending friends in a social network),
biological field (deducing connections between proteins for
treating new diseases) and community detection (grouping users of a
social network according to their interests) by leveraging the
latent information of networks. An active and important area of
current interest is to come out with algorithms that learn features
by embedding nodes or (sub)graphs into a vector space. These tasks
come under the broad umbrella of representation learning. A
representation learning model learns a mapping function that
transforms the graphs' structure information to a
low-/high-dimension vector space maintaining all the relevant
properties.
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