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020 _a9783031015885
_9978-3-031-01588-5
024 7 _a10.1007/978-3-031-01588-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aHamilton, William L.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979649
245 1 0 _aGraph Representation Learning
_h[electronic resource] /
_cby William L. Hamilton.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXVII, 141 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Background and Traditional Approaches -- Neighborhood Reconstruction Methods -- Multi-Relational Data and Knowledge Graphs -- The Graph Neural Network Model -- Graph Neural Networks in Practice -- Theoretical Motivations -- Traditional Graph Generation Approaches -- Deep Generative Models -- Conclusion -- Bibliography -- Author's Biography .
520 _aGraph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979650
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_979651
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000331
776 0 8 _iPrinted edition:
_z9783031004605
776 0 8 _iPrinted edition:
_z9783031027161
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979652
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01588-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c84820
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