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@ -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}