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 9511000 of 1860 papers

TitleStatusHype
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative PositionsCode0
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled GraphsCode0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
POWN: Prototypical Open-World Node ClassificationCode0
A Survey on Fairness for Machine Learning on GraphsCode0
Predicting Properties of Nodes via Community-Aware FeaturesCode0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Predict then Propagate: Graph Neural Networks meet Personalized PageRankCode0
Pre-train and Learn: Preserve Global Information for Graph Neural NetworksCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Trajectory Encoding Temporal Graph NetworksCode0
Preventing Representational Rank Collapse in MPNNs by Splitting the Computational GraphCode0
HATS: A Hierarchical Graph Attention Network for Stock Movement PredictionCode0
Generative-Contrastive Heterogeneous Graph Neural NetworkCode0
Graph Neural Networks with convolutional ARMA filtersCode0
Progressive Graph Convolutional Networks for Semi-Supervised Node ClassificationCode0
Connector 0.5: A unified framework for graph representation learningCode0
Transfer Entropy in Graph Convolutional Neural NetworksCode0
Prototype-based Interpretable Graph Neural NetworksCode0
Geometric deep learning on graphs and manifolds using mixture model CNNsCode0
DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-DecouplingCode0
A Unification Framework for Euclidean and Hyperbolic Graph Neural NetworksCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
PushNet: Efficient and Adaptive Neural Message PassingCode0
Geometric instability of graph neural networks on large graphsCode0
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social NetworksCode0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet ConvolutionCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Geometric Scattering Attention NetworksCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Geometry Contrastive Learning on Heterogeneous GraphsCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention NetworksCode0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Random Walk Guided Hyperbolic Graph DistillationCode0
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural NetworksCode0
HeMI: Multi-view Embedding in Heterogeneous GraphsCode0
RDF-star2Vec: RDF-star Graph Embeddings for Data MiningCode0
Dynamic Graph Representation Learning via Self-Attention NetworksCode0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis0
Topological based classification of paper domains using graph convolutional networks0
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification0
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport0
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks0
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance0
Effective backdoor attack on graph neural networks in link prediction tasks0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only0
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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