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Heterogeneous Graph Representation Learning and Applications (Hardcover, 1st ed. 2022)
Loot Price: R4,787
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Heterogeneous Graph Representation Learning and Applications (Hardcover, 1st ed. 2022)
Series: Artificial Intelligence: Foundations, Theory, and Algorithms
Expected to ship within 10 - 15 working days
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Representation learning in heterogeneous graphs (HG) is intended to
provide a meaningful vector representation for each node so as to
facilitate downstream applications such as link prediction,
personalized recommendation, node classification, etc. This task,
however, is challenging not only because of the need to incorporate
heterogeneous structural (graph) information consisting of multiple
types of node and edge, but also the need to consider heterogeneous
attributes or types of content (e.g. text or image) associated with
each node. Although considerable advances have been made in
homogeneous (and heterogeneous) graph embedding, attributed graph
embedding and graph neural networks, few are capable of
simultaneously and effectively taking into account heterogeneous
structural (graph) information as well as the heterogeneous content
information of each node. In this book, we provide a comprehensive
survey of current developments in HG representation learning. More
importantly, we present the state-of-the-art in this field,
including theoretical models and real applications that have been
showcased at the top conferences and journals, such as TKDE, KDD,
WWW, IJCAI and AAAI. The book has two major objectives: (1) to
provide researchers with an understanding of the fundamental issues
and a good point of departure for working in this rapidly expanding
field, and (2) to present the latest research on applying
heterogeneous graphs to model real systems and learning structural
features of interaction systems. To the best of our knowledge, it
is the first book to summarize the latest developments and present
cutting-edge research on heterogeneous graph representation
learning. To gain the most from it, readers should have a basic
grasp of computer science, data mining and machine learning.
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