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 401450 of 1860 papers

TitleStatusHype
Bayesian Graph Neural Networks with Adaptive Connection SamplingCode1
Deep Graph Contrastive Representation LearningCode1
Network Together: Node Classification via Cross-Network Deep Network EmbeddingCode1
Non-Local Graph Neural NetworksCode1
Graph Random Neural Network for Semi-Supervised Learning on GraphsCode1
Understanding Negative Sampling in Graph Representation LearningCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Semi-supervised Hypergraph Node Classification on Hypergraph Line ExpansionCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Principal Neighbourhood Aggregation for Graph NetsCode1
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional NetworksCode1
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on GraphsCode1
A Survey of Adversarial Learning on GraphsCode1
Inductive Representation Learning on Temporal GraphsCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Unifying Graph Convolutional Neural Networks and Label PropagationCode1
Disease State Prediction From Single-Cell Data Using Graph Attention NetworksCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
Bilinear Graph Neural Network with Neighbor InteractionsCode1
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph EmbeddingCode1
Graph Representation Learning via Graphical Mutual Information MaximizationCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
Graph-Bert: Only Attention is Needed for Learning Graph RepresentationsCode1
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Diffusion Improves Graph LearningCode1
Hyperbolic Graph Convolutional Neural NetworksCode1
Graph Convolutional Networks for Road NetworksCode1
Adversarial Training Methods for Network EmbeddingCode1
DropEdge: Towards Deep Graph Convolutional Networks on Node ClassificationCode1
Node Attribute Generation on GraphsCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Signed Graph Attention NetworksCode1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
Gated Graph Convolutional Recurrent Neural NetworksCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Simplifying Graph Convolutional NetworksCode1
Streaming Graph Neural NetworksCode1
How Powerful are Graph Neural Networks?Code1
Deep Graph InfomaxCode1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
FastGCN: Fast Learning with Graph Convolutional Networks via Importance SamplingCode1
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
Graph Attention NetworksCode1
Inductive Representation Learning on Large GraphsCode1
Neural Message Passing for Quantum ChemistryCode1
Show:102550
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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