Papers › Variational Graph Auto-Encoders

Variational Graph Auto-Encoders

21 Nov 2016arXiv:1611.07308archive 2025-07-28

Thomas N. Kipf, Max Welling

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.

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Code

Syntology Ran 7 of 13 code samples harvested from 4 repositories linked to this paper; 6 have no recorded run. Of those that ran: 3 ran · honoured contract; 3 ran · our draft was wrong; 1 ran · fixture could not drive it.

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22 repositories listed; official and paper-mentioned ones first.

tkipf/gae officialmentioned on GitHubtf report
DaehanKim/vgae_pytorch mentioned on GitHubpytorch report
JuliaSun623/VGAE_dgl mentioned on GitHubpytorch report
Monti03/VGAE mentioned on GitHubtf report
MysteryVaibhav/DW-GAE mentioned on GitHubpytorchMIT report
Omairss/RepresentationLearning mentioned on GitHubtfMIT report
flawless1202/vgae_pyg mentioned on GitHubpytorch report
leffff/vgae-pytorch mentioned on GitHubpytorchMIT report
lfhase/ciga mentioned on GitHubpytorch report
qkrdmsghk/GOODHSE mentioned on GitHubpytorch report
xiyou3368/DGVAE mentioned on GitHubtf report
zfjsail/gae-pytorch mentioned on GitHubpytorchMIT report
dmlc/dgl pytorch report

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Code Syntology ran Syntology

13 samples harvested; 7 ran; 3 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran · honoured contract
3ran · our draft was wrong
1ran · fixture could not drive it
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construct_feed_dict Omairss/RepresentationLearning/src/preprocessing.py community (archive-listed) ran · fixture could not drive it MIT (permissive) · b959bc9ff832fac0 · report
dot_product_decode DaehanKim/vgae_pytorch/model.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · ae488d391f6d24c2 · report
get_layer_uid Omairss/RepresentationLearning/src/layers.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · addac9c28c065096 · report
glorot_init DaehanKim/vgae_pytorch/model.py community (archive-listed) ran · honoured contract fingerprinted MIT (permissive) · 33c8d926f0ebceb3 · report
parse_index_file Omairss/RepresentationLearning/src/input_data.py community (archive-listed) ran · honoured contract MIT (permissive) · 5c3fa9402a9405bc · report
preprocess_graph Omairss/RepresentationLearning/src/preprocessing.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 20362feefced8682 · report
sparse_to_tuple Omairss/RepresentationLearning/src/preprocessing.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 3965c8f8e2d0023e · report
adj_matrix_from_edge_index leffff/vgae-pytorch/utils.py community (archive-listed) unverified MIT (permissive) · 978d23ad9dfa53fb · report
dropout_sparse Omairss/RepresentationLearning/src/layers.py community (archive-listed) unverified MIT (permissive) · 1aae0eaba6789dd0 · report
load_data Omairss/RepresentationLearning/src/input_data.py community (archive-listed) unverified MIT (permissive) · a90896d151bea6b9 · report
load_data zfjsail/gae-pytorch/gae/utils.py community (archive-listed) unverified MIT (permissive) · 5e07de06a8aff025 · report
loss_function zfjsail/gae-pytorch/gae/optimizer.py community (archive-listed) unverified MIT (permissive) · cf61291f93016fe4 · report
weight_variable_glorot Omairss/RepresentationLearning/src/initializations.py community (archive-listed) unverified MIT (permissive) · 8a99ee5c26e527fa · report

Tasks

DecoderGraph ClusteringLink PredictionPrediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Clustering Citeseer GAE ACC 40.8 #8 of 9 Archive leaderboard report
Graph Clustering Cora GAE ACC 59.6 #8 of 9 Archive leaderboard report
Graph Clustering Pubmed VGAE ACC 65.48 #6 of 7 Archive leaderboard report
Link Prediction Citeseer Variational graph auto-encoders ACC 91.4 #13 of 13 Archive leaderboard report
Link Prediction Cora Variational graph auto-encoders ACC 92.0 #13 of 13 Archive leaderboard report
Link Prediction Pubmed Variational graph auto-encoders ACC 97.1 #13 of 13 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: VGAE

VGAE

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