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

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Showing 1701–1750 of 1860 papers

TitleStatusHype
SAS: A Simple, Accurate and Scalable Node Classification Algorithm—0
Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling—0
Scalable Deep Metric Learning on Attributed Graphs—0
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search—0
Seastar: vertex-centric programming for graph neural networks—0
SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods—0
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks—0
Self-attention Dual Embedding for Graphs with Heterophily—0
Self-Directed Learning of Convex Labelings on Graphs—0
Self-Explainable Graph Neural Networks for Link Prediction—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Self-supervised Incremental Deep Graph Learning for Ethereum Phishing Scam Detection—0
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath—0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks—0
SAIL: Self-Augmented Graph Contrastive Learning—0
Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos—0
Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification—0
Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification—0
Semi-Supervised Hierarchical Graph Classification—0
Semi-Supervised Learning on Graphs Based on Local Label Distributions—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks—0
Semi-Supervised Tensor Factorization for Node Classification in Complex Social Networks—0
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network—0
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise—0
Sheaf Hypergraph Networks—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Shift-Robust Node Classification via Graph Adversarial Clustering—0
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data—0
Signed Graph Neural Networks: A Frequency Perspective—0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training—0
Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs—0
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Simple yet Effective Graph Distillation via Clustering—0
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective—0
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity—0
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily—0
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks—0
Space-Invariant Projection in Streaming Network Embedding—0
Sparse Decomposition of Graph Neural Networks—0
Sparse Diffusion-Convolutional Neural Networks—0
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks—0
SpecSphere: Dual-Pass Spectral-Spatial Graph Neural Networks with Certified Robustness—0
Spectral Embedding of Graph Networks—0
Spectral Graph Attention Network with Fast Eigen-approximation—0
Spectral Greedy Coresets for Graph Neural Networks—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
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All datasetsCiteseerPubmedCoraCiteSeer with Public Split: fixed 20 nodes per classPubMed with Public Split: fixed 20 nodes per classCora with Public Split: fixed 20 nodes per classPPICoauthor CSCora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09—Unverified
2SplineCNNAccuracy79.2—Unverified
3PathNetAccuracy (%)77.98—Unverified
43ferenceAccuracy76.33—Unverified
5MMAAccuracy76.3—Unverified
6PPNPAccuracy75.83—Unverified
7CoLinkDistAccuracy75.79—Unverified
8CoLinkDistMLPAccuracy75.77—Unverified
9APPNPAccuracy75.73—Unverified
10CleoraAccuracy75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21—Unverified
2CoLinkDistAccuracy89.58—Unverified
3CoLinkDistMLPAccuracy89.53—Unverified
4PathNetAccuracy (%)88.92—Unverified
53ferenceAccuracy88.9—Unverified
6SplineCNNAccuracy88.88—Unverified
7LinkDistAccuracy88.86—Unverified
8LinkDistMLPAccuracy88.79—Unverified
9PairEF188.57—Unverified
10GCN + MixupAccuracy87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24—Unverified
2CoLinkDistAccuracy87.89—Unverified
33ferenceAccuracy87.78—Unverified
4LinkDistMLPAccuracy87.58—Unverified
5CoLinkDistMLPAccuracy87.54—Unverified
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10DifNetAccuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GGCMAccuracy74.2—Unverified
6GEMAccuracy74.2—Unverified
7Truncated KrylovAccuracy73.86—Unverified
8SSGCAccuracy73.6—Unverified
9OKDEEMAccuracy73.53—Unverified
10GCNIIAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4—Unverified
2CPF-tra-GCNIIAccuracy83.2—Unverified
3DSGCNAccuracy81.9—Unverified
4SuperGAT MXAccuracy81.7—Unverified
5Truncated KrylovAccuracy81.7—Unverified
6G-APPNPAccuracy80.95—Unverified
7GGCMAccuracy80.8—Unverified
8GCN(predicted-targets)Accuracy80.42—Unverified
9SSGCAccuracy80.4—Unverified
10GCNIIAccuracy80.2—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9—Unverified
2GCN-TVAccuracy86.3—Unverified
3GCNIIAccuracy85.5—Unverified
4CPF-ind-APPNPAccuracy85.3—Unverified
5AIR-GCNAccuracy84.7—Unverified
6H-GCNAccuracy84.5—Unverified
7G-APPNPAccuracy84.31—Unverified
8SuperGAT MXAccuracy84.3—Unverified
9DSGCNAccuracy84.2—Unverified
10LDS-GNNAccuracy84.1—Unverified