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 201250 of 1860 papers

TitleStatusHype
Graph Propagation Transformer for Graph Representation LearningCode1
Exploring Graph Tasks with Pure LLMs: A Comprehensive Benchmark and InvestigationCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein ApproximationCode1
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and RethinkingCode1
Beyond Homophily: Structure-aware Path Aggregation Graph Neural NetworkCode1
Explaining the Explainers in Graph Neural Networks: a Comparative StudyCode1
Beyond Low-frequency Information in Graph Convolutional NetworksCode1
Exphormer: Sparse Transformers for GraphsCode1
Graph Stochastic Neural Networks for Semi-supervised LearningCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural NetworksCode1
Adversarial Training Methods for Network EmbeddingCode1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
CAT-Walk: Inductive Hypergraph Learning via Set WalksCode1
Diffusion Improves Graph LearningCode1
Bilinear Graph Neural Network with Neighbor InteractionsCode1
Heterogeneous Graph Tree NetworksCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Binary Graph Convolutional Network with Capacity ExplorationCode1
Accelerating Large Scale Real-Time GNN Inference using Channel PruningCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial ComplexesCode1
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node SamplingCode1
Multi-hop Attention Graph Neural NetworkCode1
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed HypergraphsCode1
Boosting Graph Embedding on a Single GPUCode1
A pipeline for fair comparison of graph neural networks in node classification tasksCode1
Boosting Multitask Learning on Graphs through Higher-Order Task AffinitiesCode1
Disease State Prediction From Single-Cell Data Using Graph Attention NetworksCode1
Hypergraph-MLP: Learning on Hypergraphs without Message PassingCode1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on HypergraphsCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing MessagesCode1
Inductive Entity Representations from Text via Link PredictionCode1
Inductive Representation Learning on Large GraphsCode1
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural NetworksCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Evaluating Node Embeddings of Complex NetworksCode1
Examining the Effects of Degree Distribution and Homophily in Graph Learning ModelsCode1
A Representation Learning Framework for Property GraphsCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation FrameworkCode1
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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
6NodeNetAccuracy86.8Unverified
7CleoraAccuracy86.8Unverified
8MMAAccuracy85.8Unverified
9GResNet(GAT)Accuracy85.5Unverified
10DifNetAccuracy85.1Unverified
#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
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