000 | 04093nam a22005895i 4500 | ||
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001 | 978-3-031-16174-2 | ||
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008 | 221116s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031161742 _9978-3-031-16174-2 |
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024 | 7 |
_a10.1007/978-3-031-16174-2 _2doi |
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_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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSynthesis Lectures on Data Mining and Knowledge Discovery, _x2151-0075 |
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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 |
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650 | 0 |
_aComputer science. _99832 |
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650 | 0 |
_aComputer science _xMathematics. _93866 |
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650 | 0 |
_aNeural networks (Computer science) . _979400 |
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650 | 0 |
_aData mining. _93907 |
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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 |
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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 |
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