Watch Your Step: Learning Node Embeddings via Graph Attention

Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random...

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Veröffentlicht in:	arXiv.org 2018-09
Hauptverfasser:	Abu-El-Haija, Sami, Perozzi, Bryan, Al-Rfou, Rami, Alemi, Alex
Format:	Artikel
Sprache:	eng
Schlagworte:	Back propagation Datasets Mathematical models Optimization Parameters Power series Random walk Random walk theory State of the art
Online-Zugang:	Volltext
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