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 11011150 of 1860 papers

TitleStatusHype
Topological Relational Learning on GraphsCode1
Barlow Graph Auto-Encoder for Unsupervised Network Embedding0
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector QuantizationCode1
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple MethodsCode1
Graph Posterior Network: Bayesian Predictive Uncertainty for Node ClassificationCode1
Deeper-GXX: Deepening Arbitrary GNNs0
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor AggregationCode0
Gophormer: Ego-Graph Transformer for Node Classification0
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
Degree-Based Random Walk Approach for Graph Embedding0
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity MappingCode0
Watermarking Graph Neural Networks based on Backdoor Attacks0
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks0
Boosting Graph Embedding on a Single GPUCode1
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs0
Graph-less Neural Networks: Teaching Old MLPs New Tricks via DistillationCode1
Label-Wise Graph Convolutional Network for Heterophilic GraphsCode0
Relation-aware Heterogeneous Graph for User ProfilingCode0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
SoGCN: Second-Order Graph Convolutional NetworksCode0
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks0
Topology-Imbalance Learning for Semi-Supervised Node ClassificationCode1
Label Propagation across Graphs: Node Classification using Graph Neural Tangent Kernels0
Graph Pointer Neural Networks0
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community InfluencesCode0
Latent Network Embedding via Adversarial Auto-encoders0
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation0
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels0
Inductive Lottery Ticket Learning for Graph Neural Networks0
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning0
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification0
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?0
Neurally boosted supervised spectral clustering0
Graph Information Matters: Understanding Graph Filters from Interaction Probability0
Crossformer: Transformer with Alternated Cross-Layer Guidance0
Equivariant Heterogeneous Graph Networks0
Stabilized Self-training with Negative Sampling on Few-labeled Graph Data0
Efficient Ensembles of Graph Neural Networks0
How Frequency Effect Graph Neural Networks0
Effective Polynomial Filter Adaptation for Graph Neural Networks0
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS0
Connecting Graph Convolution and Graph PCA0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
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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
10TransGNN1:1 Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GEMAccuracy74.2Unverified
6GGCMAccuracy74.2Unverified
7Truncated KrylovAccuracy73.86Unverified
8SSGCAccuracy73.6Unverified
9OKDEEMAccuracy73.53Unverified
10GCNIIAccuracy73.4Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4Unverified
2CPF-tra-GCNIIAccuracy83.2Unverified
3DSGCNAccuracy81.9Unverified
4Truncated KrylovAccuracy81.7Unverified
5SuperGAT MXAccuracy81.7Unverified
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