Papers › Revisiting Semi-Supervised Learning with Graph Embeddings

Revisiting Semi-Supervised Learning with Graph Embeddings

29 Mar 2016arXiv:1603.08861archive 2025-07-28

Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov

We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.

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Code

Syntology Ran 15 of 28 code samples harvested from 10 repositories linked to this paper; 13 have no recorded run. Of those that ran: 3 ran · honoured contract; 2 ran · violated contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 8 ran with no contract checked.

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DeepGraphLearning/GMNN mentioned on GitHubpytorch report
Maysir/GRCN mentioned on GitHubpytorch report
ZhuangCY/Coding-NN mentioned on GitHub report
ZhuangCY/DGCN mentioned on GitHub report
agiresearch/InstructGLM mentioned on GitHubpytorch report
ajbisberg/gcn mentioned on GitHubtf report
asarigun/la-gcn-pytorch mentioned on GitHubpytorch report
asarigun/la-gcn-tensorflow mentioned on GitHubtfMIT report
asarigun/nfc mentioned on GitHubtfMIT report
asarigun/nfc-gcn mentioned on GitHubtf report
dfdazac/dgi mentioned on GitHubtf report
firojalam/domain-adaptation mentioned on GitHubtf report
iMoonLab/DHGNN mentioned on GitHubpytorch report
jiangboahu/glcn-tf mentioned on GitHubtf report
kimiyoung/planetoid mentioned on GitHubMIT report
meliketoy/graph-cnn.pytorch mentioned on GitHubpytorch report
mipot101/GNM-Implementations mentioned on GitHubpytorch report
plusross/grcn mentioned on GitHubpytorch report
tkipf/gcn mentioned on GitHubtfMIT report
tkipf/ica mentioned on GitHub report
venomouscyanide/S3GRL_OGB mentioned on GitHubpytorch report
venomouscyanide/s3grl mentioned on GitHubpytorch report
venomouscyanide/s3grl_ogb mentioned on GitHubpytorch report
wokas36/DFNets mentioned on GitHubtfMIT report

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2ran · violated contract
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1ran · fixture could not drive it
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GCNConv_dense Maysir/GRCN/models/GRCN.py community (archive-listed) ran MIT (permissive) · ec355dcbf751cc7a · report
GCNConv_diag Maysir/GRCN/models/GRCN.py community (archive-listed) ran MIT (permissive) · 270166c76b26a9ee · report
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ICA tkipf/ica/ica/classifiers.py community (archive-listed) ran MIT (permissive) · bf9e124488a3ad49 · report
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Model asarigun/nfc-gcn/models.py community (archive-listed) ran MIT (permissive) · 79092f3060750964 · report
dot asarigun/la-gcn-tensorflow/layers.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT (permissive) · bc3a2072a2a9cca3 · report
get_class tkipf/ica/ica/classifiers.py community (archive-listed) ran · our draft was wrong MIT (permissive) · b8337cff1a162df4 · report
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Tasks

Document ClassificationEntity Extraction using GANGeneral ClassificationNode ClassificationText Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Document Classification Cora Planetoid* Accuracy 75.7% #5 of 6 Archive leaderboard report
Node Classification Citeseer Planetoid* Accuracy 64.7% #65 of 71 Archive leaderboard report
Node Classification Cora Planetoid* Accuracy 75.7% #70 of 73 Archive leaderboard report
Node Classification NELL Planetoid* Accuracy 61.9% #4 of 4 Archive leaderboard report
Node Classification Pubmed Planetoid* Accuracy 77.2% #61 of 70 Archive leaderboard report
Node Classification USA Air-Traffic Planetoid* Accuracy 64.7 #3 of 7 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.

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