From 9788babff237673ca5fa168fc6fb2218c5beccdc Mon Sep 17 00:00:00 2001 From: Louis Date: Thu, 12 Jun 2025 14:54:01 +0200 Subject: [PATCH] Modif references --- references.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/references.bib b/references.bib index b85774d..cc848d9 100644 --- a/references.bib +++ b/references.bib @@ -722,7 +722,7 @@ Read\_Status\_Date: 2025-05-14T20:18:00.025Z}, urldate = {2025-05-09}, abstract = {We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.}, pubstate = {prepublished}, - keywords = {/unread,Computer Science - Machine Learning,Statistics - Machine Learning}, + keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}, annotation = {Read\_Status: New\\ Read\_Status\_Date: 2025-05-09T11:54:37.094Z}, file = {/home/louis/snap/zotero-snap/common/Zotero/storage/5THEWLW6/Kipf et Welling - 2016 - Variational Graph Auto-Encoders.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/BBTHQNRZ/1611.html}