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.
The study of environment is a must for all of us as we are an integral part of the environment. It includes a composite and exhaustive study of physical and biological sciences including subjects, such as Ecology, Botany, Zoology, Physics, Chemistry, Social Science, Geography, etc. Not only this, a study of environment also includes human relationship, perception and policies towards environment.Hence, in order to understand and learn more about the environment in which we live in and to find answers to all our queries regarding the mysteries that surround the environment, this Quiz Book, is an ideal one.The environment Quiz Book has many interesting features like simple questions and one-line or one-word answers, Fill in the Blanks, Multiple Choice Questions(MCQs), Crossword Puzzles, word search, ect. all with solutions to make it more appealing to readers of all ages, particularly the school and college students. So read on to know more about the environment that surrounds us... #v&spublishers
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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