000			01149nam a22002657a 4500
003			IIITD
005			20250620020003.0
008			240425b |||||||| |||| 00| 0 eng d
020			_a9783031004605
040			_aIIITD
082			_a006.3 _bHAM-G
100			_aHamilton, William L.
245			_aGraph representation learning _cby William L. Hamilton.
260			_aNew York : _bSpringer, _c©2022
300			_axvii, 141 p. : _bill. ; _c23 cm.
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
650			_adeep learning
650			_a geometric deep learning
650			_a node embeddings
700			_aBrachman, Ronld _eeditor
700			_aRossi, Francesca _eeditor
700			_aStone, Peter _eeditor
942			_2ddc _cBK _05
999			_c172588 _d172588