SOTAVerified

Node Classification

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

Papers

Showing 10011050 of 1860 papers

TitleStatusHype
View-Consistent Heterogeneous Network on Graphs With Few Labeled NodesCode1
Supervised Contrastive Learning with Structure Inference for Graph Classification0
GRAND+: Scalable Graph Random Neural NetworksCode1
Shift-Robust Node Classification via Graph Adversarial Clustering0
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervisionCode0
Deep Partial Multiplex Network Embedding0
R-GCN: The R Could Stand for RandomCode1
Graph Representation Learning Beyond Node and HomophilyCode0
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks0
GAP: Differentially Private Graph Neural Networks with Aggregation PerturbationCode1
Graph Attention RetrospectiveCode1
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network DatasetCode1
Exploring Edge Disentanglement for Node Classification0
Graph Masked Autoencoders with TransformersCode1
When Does A Spectral Graph Neural Network Fail in Node Classification?0
Random Laplacian Features for Learning with Hyperbolic SpaceCode1
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network0
Geometric Graph Representation Learning via Maximizing Rate Reduction0
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNsCode1
Simplified Graph Convolution with HeterophilyCode0
FMP: Toward Fair Graph Message Passing against Topology Bias0
Bandit Sampling for Multiplex Networks0
Neural Models for Output-Space Invariance in Combinatorial Problems0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification0
MarkovGNN: Graph Neural Networks on Markov DiffusionCode1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, RevisitedCode1
Graph Coloring with Physics-Inspired Graph Neural NetworksCode1
Investigating Transfer Learning in Graph Neural Networks0
When Do Flat Minima Optimizers Work?Code1
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Learning Robust Representation through Graph Adversarial Contrastive Learning0
Graph Representation Learning via Aggregation EnhancementCode1
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional NetworksCode1
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification0
Partition-Based Active Learning for Graph Neural NetworksCode0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach0
Fair Node Representation Learning via Adaptive Data Augmentation0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning0
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels0
Decoupling the Depth and Scope of Graph Neural NetworksCode1
Towards Unsupervised Deep Graph Structure LearningCode1
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot LearningCode0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet ConvolutionCode0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks0
Neighbor2vec: an efficient and effective method for Graph Embedding0
MGAE: Masked Autoencoders for Self-Supervised Learning on GraphsCode1
Towards Similarity-Aware Time-Series ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09Unverified
2SplineCNNAccuracy79.2Unverified
3PathNetAccuracy (%)77.98Unverified
43ferenceAccuracy76.33Unverified
5MMAAccuracy76.3Unverified
6PPNPAccuracy75.83Unverified
7CoLinkDistAccuracy75.79Unverified
8CoLinkDistMLPAccuracy75.77Unverified
9APPNPAccuracy75.73Unverified
10CleoraAccuracy75.7Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21Unverified
2CoLinkDistAccuracy89.58Unverified
3CoLinkDistMLPAccuracy89.53Unverified
4PathNetAccuracy (%)88.92Unverified
53ferenceAccuracy88.9Unverified
6SplineCNNAccuracy88.88Unverified
7LinkDistAccuracy88.86Unverified
8LinkDistMLPAccuracy88.79Unverified
9PairEF188.57Unverified
10GCN + MixupAccuracy87.9Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24Unverified
2CoLinkDistAccuracy87.89Unverified
33ferenceAccuracy87.78Unverified
4LinkDistMLPAccuracy87.58Unverified
5CoLinkDistMLPAccuracy87.54Unverified
6CleoraAccuracy86.8Unverified
7NodeNetAccuracy86.8Unverified
8MMAAccuracy85.8Unverified
9GResNet(GAT)Accuracy85.5Unverified
10DifNetAccuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GGCMAccuracy74.2Unverified
6GEMAccuracy74.2Unverified
7Truncated KrylovAccuracy73.86Unverified
8SSGCAccuracy73.6Unverified
9OKDEEMAccuracy73.53Unverified
10GCNIIAccuracy73.4Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4Unverified
2CPF-tra-GCNIIAccuracy83.2Unverified
3DSGCNAccuracy81.9Unverified
4SuperGAT MXAccuracy81.7Unverified
5Truncated KrylovAccuracy81.7Unverified
6G-APPNPAccuracy80.95Unverified
7GGCMAccuracy80.8Unverified
8GCN(predicted-targets)Accuracy80.42Unverified
9SSGCAccuracy80.4Unverified
10GCNIIAccuracy80.2Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9Unverified
2GCN-TVAccuracy86.3Unverified
3GCNIIAccuracy85.5Unverified
4CPF-ind-APPNPAccuracy85.3Unverified
5AIR-GCNAccuracy84.7Unverified
6H-GCNAccuracy84.5Unverified
7G-APPNPAccuracy84.31Unverified
8SuperGAT MXAccuracy84.3Unverified
9DSGCNAccuracy84.2Unverified
10LDS-GNNAccuracy84.1Unverified