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 14511500 of 1860 papers

TitleStatusHype
Hierarchical graph sampling based minibatch learning with chain preservation and variance reductionCode0
Graphite: Iterative Generative Modeling of GraphsCode0
Addressing Heterophily in Node Classification with Graph Echo State NetworksCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Progressive Graph Convolutional Networks for Semi-Supervised Node ClassificationCode0
Graph Fourier Transformer with Structure-Frequency InformationCode0
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural NetworksCode0
NetEffect: Discovery and Exploitation of Generalized Network EffectsCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
GraphFM: A Comprehensive Benchmark for Graph Foundation ModelCode0
Prototype-based Interpretable Graph Neural NetworksCode0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel ApproachCode0
AdaGCN: Adaboosting Graph Convolutional Networks into Deep ModelsCode0
A Unification Framework for Euclidean and Hyperbolic Graph Neural NetworksCode0
HoloNets: Spectral Convolutions do extend to Directed GraphsCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
PushNet: Efficient and Adaptive Neural Message PassingCode0
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge GraphsCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Force-directed graph embedding with hops distanceCode0
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet ConvolutionCode0
How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node EmbeddingsCode0
Deep Generative Models for Subgraph PredictionCode0
Graph Few-shot Learning with Task-specific StructuresCode0
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent PriorsCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Graph Few-shot Learning via Knowledge TransferCode0
Graph Entropy Minimization for Semi-supervised Node ClassificationCode0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Graph Embedding on Biomedical Networks: Methods, Applications, and EvaluationsCode0
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature LearningCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational InferenceCode0
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
Symbolic Hyperdimensional Vectors with Sparse Graph Convolutional Neural NetworksCode0
Hyperedge Anomaly Detection with Hypergraph Neural NetworkCode0
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link PredictionCode0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Graph Convolutional Neural Networks via ScatteringCode0
Graph Convolutional Networks with EigenPoolingCode0
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
Hypergraph Neural Networks for Hypergraph MatchingCode0
Random Walk Guided Hyperbolic Graph DistillationCode0
Graph Contrastive Learning with Implicit AugmentationsCode0
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural NetworksCode0
Show:102550
← PrevPage 30 of 38Next →

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
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