000 | 01149nam a22002657a 4500 | ||
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003 | IIITD | ||
005 | 20240808020004.0 | ||
008 | 240425b |||||||| |||| 00| 0 eng d | ||
020 | _a9783031004605 | ||
040 | _aIIITD | ||
082 |
_a006.3 _bHAM-G |
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100 | _aHamilton, William L. | ||
245 |
_aGraph representation learning _cby William L. Hamilton. |
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260 |
_aNew York : _bSpringer, _c©2022 |
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300 |
_axvii, 141 p. : _bill. ; _c23 cm. |
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501 | _aIncluded bibilographical refferences. | ||
505 |
_t1. Introduction _t2. Background and traditional approaches _tpart I. Node embeddings. 3. Neighborhood reconstruction methods 4. Multi-relational data and knowledge graphs _tpart II. Graph neural networks. 5. The graph neural network model 6. Graph neural networks in practice _tpart III. Generative graph models. 8. Traditional graph generation approaches 9. Deep generative models |
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650 | _adeep learning | ||
650 | _a geometric deep learning | ||
650 | _a node embeddings | ||
700 |
_aBrachman, Ronld _eeditor |
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700 |
_aRossi, Francesca _eeditor |
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700 |
_aStone, Peter _eeditor |
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942 |
_2ddc _cBK _02 |
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999 |
_c172588 _d172588 |