GraphSAGE ist eine der bekanntesten Aggregate-Combine-Graph-Neural-Network-Architekturen, die durch Neighborhood-Sampling bisherige Skalierungsprobleme löst
final, da GAT mit GraphSAGE aus diesem Paper vergleicht und es wichtige Informationen zu den Datensätzen, mit denen auch GAT arbeitete, liefert
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%0 Journal Article
%1 hamilton
%A Hamilton, Will
%A Ying, Zhitao
%A Leskovec, Jure
%D 2017
%I Curran Associates, Inc.
%J Advances in Neural Information Processing Systems
%K
%P 1024-1034
%T Inductive Representation Learning on Large Graphs
@article{hamilton,
added-at = {2020-01-17T13:30:25.000+0100},
author = {Hamilton, Will and Ying, Zhitao and Leskovec, Jure},
biburl = {https://www.bibsonomy.org/bibtex/2e8afa0739c75f78337a0e82484465564/denklu},
interhash = {ad3fac0c6e1225815882f395bd58201d},
intrahash = {e8afa0739c75f78337a0e82484465564},
journal = {Advances in Neural Information Processing Systems},
keywords = {},
pages = {1024-1034},
publisher = {Curran Associates, Inc.},
timestamp = {2020-01-17T13:30:25.000+0100},
title = {Inductive Representation Learning on Large Graphs},
year = 2017
}