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 12011250 of 1860 papers

TitleStatusHype
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein ApproximationCode1
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction0
Large-Scale Network Embedding in Apache Spark0
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection0
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection0
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods0
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing MessagesCode1
On the approximation capability of GNNs in node classification/regression tasksCode0
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification0
Zero-shot Node Classification with Decomposed Graph Prototype NetworkCode1
Noise-robust Graph Learning by Estimating and Leveraging Pairwise InteractionsCode0
Node Classification Meets Link Prediction on Knowledge Graphs0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
Is Homophily a Necessity for Graph Neural Networks?0
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNsCode2
Learning Based Proximity Matrix Factorization for Node EmbeddingCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Automated Self-Supervised Learning for GraphsCode1
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange0
Vertex-Centric Visual Programming for Graph Neural Networks0
Fairness-Aware Node Representation Learning0
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian ApproachCode2
Multiple Kernel Representation Learning on NetworksCode0
XBNet : An Extremely Boosted Neural NetworkCode1
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled GraphsCode1
Graph-MLP: Node Classification without Message Passing in GraphCode1
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled GraphsCode0
Pseudo-Riemannian Graph Convolutional NetworksCode1
Graph Belief Propagation NetworksCode0
On Local Aggregation in Heterophilic Graphs0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data0
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Embedding Knowledge Graphs Attentive to Positional and Centrality QualitiesCode0
Mixup for Node and Graph ClassificationCode1
Relational Graph Neural Network Design via Progressive Neural Architecture Search0
_2-norm Flow Diffusion in Near-Linear Time0
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily0
Local, global and scale-dependent node rolesCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification0
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative PositionsCode0
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
A Robust and Generalized Framework for Adversarial Graph EmbeddingCode0
Auxiliary learning induced graph convolutional networks0
Ultrahyperbolic Neural Networks0
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?0
Free Energy Node Embedding via Generalized Skip-gram with Negative SamplingCode0
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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