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020 _a9783031161742
_9978-3-031-16174-2
024 7 _a10.1007/978-3-031-16174-2
_2doi
050 4 _aQA166-166.247
072 7 _aPBV
_2bicssc
072 7 _aMAT008000
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072 7 _aPBV
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082 0 4 _a511.5
_223
100 1 _aShi, Chuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979399
245 1 0 _aAdvances in Graph Neural Networks
_h[electronic resource] /
_cby Chuan Shi, Xiao Wang, Cheng Yang.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXIV, 198 p. 41 illus., 36 illus. in color.
_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 Data Mining and Knowledge Discovery,
_x2151-0075
505 0 _aIntroduction -- Fundamental Graph Neural Networks -- Homogeneous Graph Neural Networks -- Heterogeneous Graph Neural Networks -- Dynamic Graph Neural Networks -- Hyperbolic Graph Neural Networks -- Distilling Graph Neural Networks -- Platforms and Practice of Graph Neural Networks -- Future Direction and Conclusion -- References. .
520 _aThis book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications. In addition, this book: Provides a comprehensive introduction to the foundations and frontiers of graph neural networks and also summarizes the basic concepts and terminology in graph modeling Utilizes graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology Presents heterogeneous graph representation learning alongside homogeneous graph representation and Euclidean graph neural networks methods .
650 0 _aGraph theory.
_93662
650 0 _aComputer science.
_99832
650 0 _aComputer science
_xMathematics.
_93866
650 0 _aNeural networks (Computer science) .
_979400
650 0 _aData mining.
_93907
650 1 4 _aGraph Theory.
_93662
650 2 4 _aComputer Science.
_99832
650 2 4 _aMathematical Applications in Computer Science.
_931683
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
650 2 4 _aData Mining and Knowledge Discovery.
_979401
700 1 _aWang, Xiao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979402
700 1 _aYang, Cheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979403
710 2 _aSpringerLink (Online service)
_979404
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031161735
776 0 8 _iPrinted edition:
_z9783031161759
776 0 8 _iPrinted edition:
_z9783031161766
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_979405
856 4 0 _uhttps://doi.org/10.1007/978-3-031-16174-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c84775
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