Articles liés à Concepts and Techniques of Graph Neural Networks
Synopsis
Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
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À propos des auteurs
Vinod Kumar received the Bachelor of Science (PCM) from the University of Allahabad, Uttar Pradesh, India in 2008 and the Master of Computer Applications in 2011. He qualified for UGC-NET (Computer Science and Applications) in 2012. He did his Ph.D. from Maulana Azad National Institute of Technology (MANIT), Bhopal (MP) in 2019. He has worked as a Project Fellow on Project “Network Simulation Testbed at MCTE, MHOW (MP)” in collaboration with the Military College of Telecommunication Engineering (MCTE), Mhow (MP), funded by the Army Technology Board. He has over 10 years of experience in research and teaching. Currently, he is working as an Associate Professor in the Computer Science and Engineering Department of Koneru Lakshmaiah Education Foundation (KL Deemed to University), Andhra Pradesh. He is an active researcher in the fields of Big Data Analytics, Web Mining, Machine Learning, Blockchain Technology, and the Internet of Things. He has published 20+ research articles in reputed journals and presented his research work at conferences.
Dharmendra Rajput has a Ph.D. (January 2015) in the area of Document Clustering from National Institute of Technology Bhopal, India. He has 9+ years’ experience in teaching, research, and industry field. Currently, he is working as an Associate Professor in School of Information Technology & Engineering, VIT University, Vellore (India). He has published 10+ reputed journals papers and 12 papers presented in international conferences. He has visited four countries (i.e., Malaysia, UK, France, and UAE) to attend the various international conferences. He received various awards from Indian Government like DST, CSIR Travel Grant, MPCST Young Scientist Fellowship.
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- ÉditeurEngineering Science Reference
- Date d'édition2023
- ISBN 10 1668469049
- ISBN 13 9781668469040
- ReliureBroché
- Langueanglais
- Nombre de pages247
- ÉditeurKumar Vinod, Rajput Dharmendra Singh
- Coordonnées du fabricantnon disponible
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students. N° de réf. du vendeur 9781668469040
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