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25. Paper methods, source links, datasets, and protocol notes

This page covers the methods registered in ModSSC. It combines:

  • src/modssc/inductive/registry.py and src/modssc/transductive/registry.py
  • paper/code notes in docs/article_code/**
  • extracted dataset/protocol evidence from the local dashboard data
  • current source/code pages or author repositories, when a reliable link could be found

Status conventions:

  • "source link" means a paper, author, lab, or repository page associated with the method.
  • "code page" means an author/lab page distributes code, but not necessarily as a GitHub repository.
  • "not found/cited" means no reliable source/code link was found in the local archive or current web check.
  • Protocol notes are compact. Rows marked "needs deeper extraction" have a paper archive entry but no complete experiment_params.md extraction yet.

25.1 Benchmark comparison policy

The benchmark source is bench/configs/best/ only. bench/configs/experiments/ remains for smoke runs, documentation examples, and development templates; it is not a benchmark source unless a campaign explicitly promotes a config into best.

Benchmark ranking uses the mean test.accuracy over the five configured seeds. macro_f1, runtime, memory, and failures are reported as diagnostics, but they do not decide the winner. Pipeline selection inside a method uses val.accuracy; only the selected pipeline's test.accuracy is used for final reporting.

Comparisons are valid only on paired cells: same dataset, modality, regime, split contract, and seed set. Inductive vs transductive compares the best selected pipeline from each family on paired cells. Classical vs neural is classified by the effective pipeline and backend, not only by the method name. Poisson comparisons use poisson_learning and poisson_mbo against GNN-family methods on paired transductive cells. Cross-modal or intermodal transfer is a success only when the transferred method beats native methods on the target modality in test.accuracy; otherwise the result should report the regime where it is competitive or fails.

25.2 Paper-fidelity status

Only paper_matched may be described as "implemented as in the paper." Current statuses are intentionally conservative:

  • paper_matched: exact article protocol, backbone/model, splits, tuning, selection rule, and source validation are matched.
  • paper_approx: the implemented algorithm follows the paper family and key controls, but at least one exact protocol element is standardized or not fully proven identical.
  • standardized_only: valid ModSSC benchmark method or control, but not an article-reproduction claim.
  • not_claimable: source/protocol extraction is incomplete or evidence is insufficient for a paper-level claim.

No current method is marked paper_matched. paper_approx and standardized_only methods may be benchmarked, but results must not be presented as exact paper reproductions.

Method Family Status Comparison class
supervised control standardized_only baseline
pseudo_label pseudo_label paper_approx inductive_classic_or_neural_by_backend
self_training self_training not_claimable inductive_classic
setred self_training_editing not_claimable inductive_classic_or_neural_by_backend
pi_model consistency_regularization paper_approx inductive_neural
fixmatch fixmatch_thresholding paper_approx inductive_neural
comatch contrastive_graph_regularization paper_approx inductive_neural
defixmatch debiasing_fixmatch paper_approx inductive_neural
daso imbalanced_ssl paper_approx inductive_neural
adsh adaptive_thresholding paper_approx inductive_neural
flexmatch adaptive_thresholding paper_approx inductive_neural
adamatch domain_adaptation_ssl paper_approx inductive_neural
free_match adaptive_thresholding paper_approx inductive_neural
softmatch adaptive_thresholding paper_approx inductive_neural
mixmatch mixup_consistency paper_approx inductive_neural
simclr_v2 self_supervised_transfer standardized_only inductive_neural
mean_teacher teacher_student_consistency paper_approx inductive_neural
meta_pseudo_labels teacher_student_pseudo_labeling paper_approx inductive_neural
temporal_ensembling consistency_regularization paper_approx inductive_neural
uda augmentation_consistency paper_approx inductive_neural
vat adversarial_consistency paper_approx inductive_neural
noisy_student teacher_student_pseudo_labeling standardized_only inductive_neural
co_training multi_view_co_training not_claimable inductive_classic_or_neural_by_backend
democratic_co_learning ensemble_co_learning not_claimable inductive_classic_or_neural_by_backend
deep_co_training deep_co_training not_claimable inductive_neural
tri_training ensemble_self_training paper_approx inductive_classic_or_neural_by_backend
trinet deep_tri_training not_claimable inductive_neural
s4vm safe_margin_ssl paper_approx inductive_classic_or_neural_by_backend
label_propagation graph_diffusion paper_approx transductive_classic
label_spreading graph_diffusion paper_approx transductive_classic
laplace_learning graph_pde not_claimable transductive_classic
lazy_random_walk random_walk not_claimable transductive_classic
dynamic_label_propagation graph_diffusion paper_approx transductive_classic
graph_mincuts graph_cut not_claimable transductive_classic
tsvm margin_ssl paper_approx transductive_classic
poisson_learning poisson_graph_pde paper_approx transductive_classic
poisson_mbo poisson_graph_pde paper_approx transductive_classic
p_laplace_learning graph_pde paper_approx transductive_classic
chebnet gnn_spectral paper_approx transductive_neural
planetoid gnn_embedding paper_approx transductive_neural
gcn gnn_message_passing paper_approx transductive_neural
graphsage gnn_message_passing paper_approx transductive_neural
gat gnn_attention paper_approx transductive_neural
sgc gnn_linearized paper_approx transductive_neural
appnp gnn_propagation paper_approx transductive_neural
h_gcn gnn_hierarchical paper_approx transductive_neural
n_gcn gnn_multiscale paper_approx transductive_neural
graphhop label_aggregation_graph paper_approx transductive_neural
grafn few_label_graph paper_approx transductive_neural
gcnii deep_gnn paper_approx transductive_neural
grand random_propagation_gnn paper_approx transductive_neural

25.3 PDF index

Every paper-backed method now has a paper_pdf value in its MethodInfo. Public direct PDF URLs are used when available; otherwise the link points to the archived PDF under docs/article_code/**. The supervised entry is a ModSSC control baseline, not a paper-backed method.

25.3.1 Inductive PDFs

Method PDF
adamatch PDF
adsh PDF
co_training PDF
comatch PDF
daso PDF
deep_co_training PDF
defixmatch PDF
democratic_co_learning local archive: docs/article_code/inductive/2004-Democratic colearning/21-2004-Democratic colearning.pdf
fixmatch PDF
flexmatch PDF
free_match PDF
mean_teacher PDF
meta_pseudo_labels PDF
mixmatch PDF
noisy_student PDF
pi_model PDF
pseudo_label local archive: docs/article_code/inductive/2013-Pseudo Label/8_pseudo_label.pdf
s4vm PDF
self_training local archive: docs/article_code/inductive/1995-Self Training/4-1995-Unsupervised word sense disambiguation rivaling supervised methods.pdf
setred local archive: docs/article_code/inductive/2005-SETRED selftraining with editing/9-2005-SETRED selftraining with editing.pdf
simclr_v2 PDF
softmatch PDF
supervised n/a: ModSSC baseline, not a paper-backed method
temporal_ensembling PDF
tri_training PDF
trinet PDF
uda PDF
vat PDF

25.3.2 Transductive PDFs

Method PDF
appnp PDF
chebnet PDF
dynamic_label_propagation PDF
gat PDF
gcn PDF
gcnii PDF
grafn PDF
grand PDF
graph_mincuts local archive: docs/article_code/transductive/2001-Graph Mincuts/Learning from Labeled and Unlabeled Data using Graph Mincuts.pdf
graphhop PDF
graphsage PDF
h_gcn PDF
label_propagation local archive: docs/article_code/transductive/2002-Label Propagation/Learning from Labeled and Unlabeled Data with Label Propagation.pdf
label_spreading local archive: docs/article_code/transductive/2004-Label Spreading/Learning with Local and Global Consistency.pdf
laplace_learning local archive: docs/article_code/transductive/2003-Laplace Learning/Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.pdf
lazy_random_walk local archive: docs/article_code/transductive/2004-Lazy Random Walk/Learning from labeled and labeled data using Random Walks.pdf
n_gcn PDF
p_laplace_learning PDF
planetoid PDF
poisson_learning PDF
poisson_mbo PDF
sgc PDF
tsvm PDF

25.4 Inductive methods

Method Source link Paper datasets Paper protocol and params
supervised none; ModSSC baseline task-dependent Control baseline, not tied to a single SSL paper. Use the same split, backbone, augmentation, optimizer, and budget as the SSL method being compared.
pseudo_label not found/cited MNIST; local data also maps CIFAR-10, CIFAR-100, STL-10, SVHN through later baselines ICML workshop paper from 2013. MNIST uses 100, 600, 1000, 3000 labels; 1000 validation samples; remaining train samples as unlabeled; 10 random splits, 30 splits for 100 labels. One hidden layer MLP, 5000 hidden units, ReLU hidden, sigmoid output; SGD plus dropout; initial LR 1.5; minibatches of 32 labeled and 256 unlabeled. Alpha schedule uses alpha_f=3, T1=100, T2=600 without DAE pretraining and T1=200, T2=800 with DAE pretraining.
self_training not found/cited breast_cancer; citation benchmarks in local data for later variants Classic self-training baseline. Paper archive exists but exact hyperparameter extraction is incomplete; compare only with explicit split/classifier settings from the selected study.
setred not found/cited breast_cancer Self-training with editing. Archive indicates repeated evaluation/fold protocols, but the exact parameter table still needs deeper extraction before claiming matched paper settings.
pi_model s-laine/tempens CIFAR-10, CIFAR-100, MNIST, STL-10, SVHN Shares the linked source with Temporal Ensembling. SVHN uses 500 labels; CIFAR-10 uses 4000 labels; CIFAR-100 can use Tiny Images as extra unlabeled data. Local extraction: wmax=100 for Pi-model, wmax=300 for CIFAR-100 plus Tiny Images; linked source notes Theano/Lasagne and dataset-specific augmentation/ZCA settings.
fixmatch google-research/fixmatch AG News, CIFAR-10, CIFAR-100, DBpedia, IMDb, MNIST, STL-10, SVHN; ImageNet in the paper Main image protocol: WRN/ResNet backbones; labeled batch B=64, unlabeled ratio mu=7, confidence threshold tau=0.95, unsupervised weight lambda_u=1, SGD momentum 0.9 with Nesterov, LR 0.03, cosine schedule. Weight decay is 0.0005 for most image datasets and 0.001 for CIFAR-100. ImageNet uses lower threshold (tau=0.7) and larger unsupervised weight (lambda_u=10).
comatch salesforce/CoMatch CIFAR-10, STL-10; ImageNet in the paper CIFAR-10 uses WRN-28-2; STL-10 uses ResNet-18; ImageNet uses ResNet-50. SGD momentum 0.9; CIFAR/STL weight decay 0.0005 and LR 0.03 cosine. Common SSL settings include lambda_cls=1, tau=0.95, mu=7, B=64, graph smoothing alpha=0.9, memory queue K=2560, contrastive temperature around 0.2, pseudo-label temperature around 0.8, and dataset-specific lambda_ctr.
defixmatch HugoSchmutz/DeFixmatch CIFAR-10, CIFAR-100, MNIST, STL-10, SVHN ICLR 2023 paper: Don't fear the unlabelled: safe semi-supervised learning via debiasing. The code implements the debiased FixMatch variant reported as DeFixmatch; use paper-specific MCAR/debiasing settings before comparing to standard FixMatch.
daso ytaek-oh/daso CIFAR-10-LT, CIFAR-100-LT, STL-10-LT; Semi-Aves in the paper Imbalanced SSL protocol built on FixMatch/ReMixMatch-style backbones. CIFAR/STL experiments use 250k iterations; Semi-Aves uses 90 epochs. Label/unlabeled imbalance ratios are swept (gamma_l, gamma_u); distribution-aware alignment uses a memory queue/prototype temperature and alignment weight selected by paper ablations.
adsh LAMDA ADSH code page CIFAR-10, STL-10, SVHN Adaptive per-class thresholding for distribution-aware SSL. Protocol sensitivity is high: paper protocol uses CNN/ResNet/WRN-style settings and random augmentation, while naive scratch ResNet baselines are not directly comparable.
flexmatch TorchSSL/TorchSSL AG News, CIFAR-10, CIFAR-100, DBpedia, IMDb, STL-10, SVHN; ImageNet in the paper FixMatch-style setup with class-adaptive thresholds. Common paper settings: mu=7, tau=0.95 for CIFAR/SVHN/STL, tau=0.7 for ImageNet, labeled batch 64 except ImageNet 128, LR 0.03, SGD momentum 0.9, EMA 0.999, lambda_u=1, WRN-28-2/28-8/37-2 or ResNet-50, weight decay 5e-4 except CIFAR-100 1e-3 and ImageNet 3e-4.
adamatch google-research/adamatch CIFAR-10, MNIST, SVHN; Digit-Five and DomainNet in the paper ICLR 2022 paper: AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation. Digit/Five and DomainNet protocols use ResNetV2-101 for 224px images, WRN-34-2 for 64px, WRN-28-2 for 32px, LR 0.03 with cosine decay, and confidence threshold around 0.9. Pretrained-domain runs often set weight decay to 0.
free_match microsoft/Semi-supervised-learning CIFAR-10, CIFAR-100, STL-10, SVHN; ImageNet in the paper Self-adaptive confidence thresholding plus fairness regularization. Uses WRN-28-2/28-8/37-2 or ResNet-50, SGD momentum 0.9, LR 0.03 cosine, EMA 0.999, labeled batch 64 except ImageNet 128, and lambda_u=1. Fairness weight is 0.01 for very-low-label settings and 0.05 otherwise; SVHN uses warmup and a bounded adaptive threshold range.
softmatch Hhhhhhao/SoftMatch AG News, CIFAR-10, CIFAR-100, DBpedia, IMDb, STL-10, SVHN; ImageNet/text settings in the paper ICLR 2023 paper: SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning. The repository is a redirect/explanation repo and points to USB/TorchSSL code entries. Uses WRN/ResNet backbones, SGD momentum 0.9, LR 0.03 cosine, EMA, labeled batch 64 and unlabeled ratio 7 for image benchmarks.
mixmatch google-research/mixmatch CIFAR-10, CIFAR-100, STL-10, SVHN WRN-28 backbones with MixUp, label guessing, sharpening, and consistency loss. Local extraction records weight decay 0.0004, STL-10 with 5000 labeled samples plus unlabeled folds, long training, and dataset-specific augmentation.
simclr_v2 google-research/simclr ImageNet in the paper; CIFAR-10 in local data Semi-supervised transfer from large self-supervised ResNet models. Paper protocol pretrains large ResNet variants, fine-tunes on 1%/10% ImageNet labels, and distills to smaller students; compare using the exact pretrained checkpoint/backbone because results are not comparable to scratch supervised training.
mean_teacher CuriousAI/mean-teacher CIFAR-10, CIFAR-100, STL-10, SVHN; ImageNet in the paper Student/teacher EMA consistency. Paper extraction covers SVHN/CIFAR/ImageNet settings, LeakyReLU conv activations, dataset-specific preprocessing/augmentation, and EMA teacher updates. Local data marks long training and VAE/embedding evidence in some baselines.
meta_pseudo_labels google-research/google-research/meta_pseudo_labels CIFAR-10, CIFAR-100, STL-10, SVHN; ImageNet and large unlabeled sources in the paper Teacher optimized by student feedback. Common settings include WRN-28-2/ResNet-50, Nesterov momentum 0.9, cosine LR, up to 1M steps for CIFAR/SVHN and 0.5M for ImageNet, tuning over short 50k-step trials, LARS fine-tuning with LR 0.001 and batch 4096, label smoothing 0.1, and dataset-specific weight decay.
temporal_ensembling s-laine/tempens CIFAR-10, CIFAR-100, SVHN Shares the linked source with Pi Model. SVHN uses 500 labels; CIFAR-10 uses 4000 labels; CIFAR-100 can use Tiny Images. Local extraction: temporal ensemble EMA alpha=0.6, wmax=30, with dataset-specific augmentation/ZCA and Theano/Lasagne versions pinned in the repo README.
uda google-research/uda AG News, CIFAR-10, CIFAR-100, CiteSeer, DBpedia, IMDb, STL-10, SVHN; ImageNet/text datasets in the paper Consistency between original and strongly augmented samples. Image settings use Nesterov SGD 0.9, cosine LR, labeled/unlabeled batches such as 64/448 on CIFAR/SVHN, and confidence filtering. ImageNet uses much larger supervised/unsupervised batches and a larger unsupervised weight in 10% label settings. Text settings use BERT-base with LR in {1e-5, 2e-5, 5e-5}, batch sizes around 32/128, dropout 0.1, and back-translation.
vat takerum/vat CIFAR-10, CIFAR-100, CiteSeer, MNIST, STL-10, SVHN Virtual adversarial regularization. Paper extraction records MNIST/CIFAR/SVHN protocols, alpha=1, supervised minibatch 64, VAT minibatch 256, Adam LR around 0.003 for MNIST and 0.001 for CNN experiments, batch norm, and LeakyReLU slope 0.1.
noisy_student google-research/noisystudent ImageNet and JFT-style unlabeled data in the paper; local data maps CIFAR-10, MNIST, SVHN for local baselines Teacher-student self-training with noise. Paper protocol uses EfficientNet teachers/students, RandAugment, dropout around 0.5, stochastic depth survival around 0.8, large unlabeled-to-labeled batch ratios, iterative pseudo-labeling, and short final fine-tuning.
co_training not found/cited CIFAR-10, CIFAR-100, CiteSeer, Cora, PubMed, SVHN in local data; original paper uses two-view classification Original setting assumes two sufficient and redundant views. Local extraction exists but mixes original and later benchmark mappings; do not compare unless the two-view feature split and base learner are specified.
democratic_co_learning not found/cited Adult Multiple learners vote with confidence estimates. Archive exists, but exact experimental parameters are not fully extracted.
deep_co_training not found/cited CIFAR-10, CIFAR-100, SVHN Two deep classifiers are regularized with adversarial examples and view disagreement. Local data marks ResNet/VAE/embedding evidence and long training; no reliable source repository was found.
tri_training not found/cited UCI-style tabular datasets in paper archive Local extraction: 12 UCI datasets, 3 classifiers, 4 unlabeled rates, repeated over 144 plotted settings. Uses three classifiers and pseudo-label agreement between two models to train the third.
trinet LAMDA Tri-net code page CIFAR-10, MNIST, SVHN IJCAI 2018 paper and LAMDA PyTorch code page. Deep tri-training variant with output smearing, diversity augmentation, and pseudo-label editing; exact table-level hyperparameters still need deeper extraction from the paper archive before matched-setting benchmarking.
s4vm LAMDA S4VM code page Adult, MNIST Safe semi-supervised SVM. LAMDA provides a code link for S4VM. Local extraction for the later multivariate-measure study records a linear SVM with C=1; compare with the selected safety-constraint and split protocol because paper variants differ.

25.5 Transductive methods

Method Source link Paper datasets Paper protocol and params
label_propagation not found/cited CIFAR-10, CiteSeer, Cora, MNIST, PubMed, SVHN in local data Classic graph propagation baseline. Paper protocols depend on the graph construction; match the same affinity/kernel, label rate, and class-mass normalization setting.
label_spreading not found/cited MNIST/USPS-style digit data Local extraction: USPS digits 1-4 with 3874 samples, alpha=0.99, RBF width 1.25 for harmonic/affinity experiments, kNN with k=1, and 100 trials. Also includes two-moons and text/web classification demonstrations.
laplace_learning not found/cited CIFAR-10 in local data; synthetic/digit/text tasks in archive Harmonic/Laplace graph SSL. Matched settings hinge on graph weights, class priors, and class-mass normalization; exact paper parameter extraction is incomplete.
lazy_random_walk not found/cited CIFAR-10 in local data Random-walk SSL baseline. Archive exists, but exact graph and walk parameters still need deeper extraction before claiming matched paper settings.
dynamic_label_propagation not found/cited MNIST Local extraction: MNIST uses 60k train and 10k test with 1% (600) and 5% (3000) labeled train samples plus 10k test samples. Object recognition settings use SIFT descriptors, 16x16 patches, stride 8, k-means codebook size 2048, chi-square distance, and 5%/10% labels. Sensitivity sweeps use alpha in [0.01, 0.1] with lambda=0.1, and lambda in [0.01, 1] with alpha=0.05.
graph_mincuts not found/cited archive-only; no reliable dataset extraction Binary graph mincut SSL. Paper archive is present, but dataset/protocol OCR is incomplete. Do not claim matched paper settings until graph construction, terminal weights, and split details are extracted.
tsvm SVMlight CiteSeer, Cora, MNIST, PubMed in local data; Reuters-21578 in local extraction The SVMlight link includes approximate training for large transductive SVMs. Local extraction records Reuters-21578 ModApte split with 9603 train and 3299 test samples. Use paper-specific SVM kernel/C settings and transductive unlabeled pool definition.
poisson_learning jwcalder/GraphLearning CIFAR-10, FashionMNIST, MNIST, WebKB ICML 2020 paper. Use the GraphLearning link to inspect Poisson Learning settings. Matched runs require the selected kNN graph, embedding/metric, weighting kernel, label rate, and dataset preprocessing.
poisson_mbo jwcalder/GraphLearning CIFAR-10, FashionMNIST, MNIST, WebKB Poisson MBO is Algorithm 2 in the Poisson Learning ICML 2020 paper, not a separate arXiv paper. Use the same paper/PDF and GraphLearning source link.
p_laplace_learning mauriciofloresML/Laplacian_Lp_Graph_SSL EMNIST, FashionMNIST, MNIST Use the linked source to inspect experiment settings. Match the paper's selected p, graph kernel, label rate, and convergence tolerance.
chebnet mdeff/cnn_graph CiteSeer, Cora, MNIST, PubMed Spectral graph CNN with Chebyshev filters. Compare with the original graph construction and polynomial order/filter settings; local data marks full-graph transductive settings.
planetoid kimiyoung/planetoid CiteSeer, Cora, PubMed; DIEL/NELL in repo data notes Linked source provides transductive and inductive demos. Standard citation protocol uses sparse features, graph adjacency, fixed train/validation/test masks, and graph/context embedding objectives.
gcn tkipf/gcn CiteSeer, Cora, PubMed Standard citation graph protocol: 20 labels per class, 500 validation nodes, 1000 test nodes; two-layer GCN, hidden size 16, dropout 0.5, L2 5e-4, Adam LR 0.01, early stopping.
graphsage williamleif/GraphSAGE Web of Science citation graph, Reddit, PPI in paper Inductive graph representation protocol. Paper uses sampled multi-hop neighborhoods, citation train/test by year, Reddit/PPI inductive splits, and TensorFlow/Adam training. Exact sampler fanouts and aggregator settings must match the selected table.
gat PetarV-/GAT CiteSeer, Cora, PubMed; PPI in paper Citation protocol follows GCN-style splits; typical paper settings use 8 attention heads, 8 hidden units per head on Cora/CiteSeer, dropout 0.6, L2 5e-4, Adam LR 0.005, ELU activations, and inductive PPI evaluation.
sgc Tiiiger/SGC CiteSeer, Cora, PubMed Simplifies GCN by removing nonlinearities and collapsing propagation before a linear classifier. Match fixed citation splits, tuned propagation order K, and the paper's regularization/LR settings.
appnp gasteigerjo/ppnp CiteSeer, Cora, PubMed Personalized PageRank propagation after prediction. Paper protocol uses repeated random splits/initializations, same architecture budget across datasets, dropout/L2/LR tuning, bootstrap confidence intervals, and paired significance testing.
h_gcn CRIPAC-DIG/H-GCN CiteSeer, Cora, PubMed Hierarchical GCN for semi-supervised node classification. Use the linked datasets/scripts to inspect settings; local data marks full-graph transductive, very-low-label, CNN/GCN-style settings.
n_gcn samihaija/mixhop CiteSeer, Cora, PubMed; PPI in paper Multi-scale GCN. Local extraction: citation splits use 20 labels/class, 500 validation, 1000 test; PPI uses 20 train graphs, 2 validation, 2 test. TensorFlow experiments use two-layer GCN/SAGE modules, hidden size 16, dropout 0.5, L2 1e-5, Adam LR 0.01, 600 steps, 20 random initializations, and sweeps over scale/radius and classifier variant.
graphhop TianXieUSC/GraphHop CiteSeer, Cora, PubMed; protein multilabel tasks in paper Iterative label aggregation/update with extremely low label rates, including one-label-per-class settings. Match hop count, label update schedule, and graph preprocessing from the linked source.
grafn Junseok0207/GraFN CiteSeer, Cora, PubMed; Amazon Computers/Photos in paper Few-label node classification with non-parametric distribution assignment. Paper settings sample support nodes per class from labeled nodes, use cosine similarity with temperature, graph augmentations, and repeated few-label splits.
gcnii chennnM/GCNII Cora, CiteSeer, PubMed, Chameleon, Cornell, Texas, Wisconsin, PPI, ogbn-arxiv Deep GCN with initial residual and identity mapping. Repo provides semi.sh, full.sh, and ppi.sh; match the script for the target table because depth and dataset-specific hyperparameters vary.
grand THUDM/GRAND Cora, CiteSeer, PubMed-style citation graphs in repo/paper NeurIPS 2020 paper. Graph Random Neural Network with random propagation and consistency regularization. Use linked scripts for propagation step count, dropout/noise, consistency loss weight, and split protocol.