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 16011650 of 1860 papers

TitleStatusHype
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network0
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications0
Exponential Family Graph Embeddings0
Heterogeneous Deep Graph InfomaxCode0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional NetworksCode0
On the choice of graph neural network architecturesCode0
A Hierarchy of Graph Neural Networks Based on Learnable Local Features0
Constant Curvature Graph Convolutional Networks0
A Capsule Network-based Model for Learning Node EmbeddingsCode0
Higher-order Weighted Graph Convolutional Networks0
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network0
Bayesian Graph Convolutional Neural Networks using Node Copying0
Composition-based Multi-Relational Graph Convolutional NetworksCode1
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
Graph Transformer NetworksCode2
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
Dynamic Graph Embedding via LSTM History Tracking0
Semi-supervisedly Co-embedding Attributed NetworksCode0
Diffusion Improves Graph LearningCode1
Hyperbolic Graph Convolutional Neural NetworksCode1
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled DataCode0
Pre-train and Learn: Preserve Global Information for Graph Neural NetworksCode0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning0
DFNets: Spectral CNNs for Graphs with Feedback-Looped FiltersCode0
Network2Vec Learning Node Representation Based on Space Mapping in Networks0
Recurrent Attention Walk for Semi-supervised ClassificationCode0
Collaborative Graph Walk for Semi-supervised Multi-Label Node ClassificationCode0
Active Learning for Graph Neural Networks via Node Feature Propagation0
DeepGCNs: Making GCNs Go as Deep as CNNsCode2
Link Prediction via Graph Attention Network0
Graph Few-shot Learning via Knowledge TransferCode0
Deep Hyperedges: a Framework for Transductive and Inductive Learning on HypergraphsCode0
Effective Stabilized Self-Training on Few-Labeled Graph DataCode0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Dynamic Joint Variational Graph Autoencoders0
Learning Robust Representations with Graph Denoising Policy Network0
On the Equivalence between Positional Node Embeddings and Structural Graph RepresentationsCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Transfer Active Learning For Graph Neural Networks0
Octave Graph Convolutional Network0
Unsupervised Learning of Node Embeddings by Detecting Communities0
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks0
Subgraph Attention for Node Classification and Hierarchical Graph Pooling0
Uncertainty-Aware Prediction for Graph Neural Networks0
Attributed Graph Learning with 2-D Graph Convolution0
Dimensional Reweighting Graph Convolution Networks0
Active Learning Graph Neural Networks via Node Feature Propagation0
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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
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