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Graph Neural Networks: Foundations, Frontiers, and Applications (Hardcover, 1st ed. 2022)
Loot Price: R3,037
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Graph Neural Networks: Foundations, Frontiers, and Applications (Hardcover, 1st ed. 2022)
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Deep Learning models are at the core of artificial intelligence
research today. It is well known that deep learning techniques are
disruptive for Euclidean data, such as images or sequence data, and
not immediately applicable to graph-structured data such as text.
This gap has driven a wave of research for deep learning on graphs,
including graph representation learning, graph generation, and
graph classification. The new neural network architectures on
graph-structured data (graph neural networks, GNNs in short) have
performed remarkably on these tasks, demonstrated by applications
in social networks, bioinformatics, and medical informatics.
Despite these successes, GNNs still face many challenges ranging
from the foundational methodologies to the theoretical
understandings of the power of the graph representation learning.
This book provides a comprehensive introduction of GNNs. It first
discusses the goals of graph representation learning and then
reviews the history, current developments, and future directions of
GNNs. The second part presents and reviews fundamental methods and
theories concerning GNNs while the third part describes various
frontiers that are built on the GNNs. The book concludes with an
overview of recent developments in a number of applications using
GNNs. This book is suitable for a wide audience including
undergraduate and graduate students, postdoctoral researchers,
professors and lecturers, as well as industrial and government
practitioners who are new to this area or who already have some
basic background but want to learn more about advanced and
promising techniques and applications.
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