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 701750 of 1860 papers

TitleStatusHype
GCNH: A Simple Method For Representation Learning On Heterophilous GraphsCode1
Multi-label Node Classification On Graph-Structured DataCode1
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual GraphsCode1
ID-MixGCL: Identity Mixup for Graph Contrastive Learning0
RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot DetectionCode1
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPTCode2
Distributional Signals for Node Classification in Graph Neural Networks0
FMGNN: Fused Manifold Graph Neural Network0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
Uncertainty Propagation in Node Classification0
BOLT: An Automated Deep Learning Framework for Training and Deploying Large-Scale Search and Recommendation Models on Commodity CPU HardwareCode0
GRAF: Graph Attention-aware Fusion NetworksCode1
Knowledge Enhanced Graph Neural Networks for Graph Completion0
FairGAT: Fairness-aware Graph Attention Networks0
Structural Imbalance Aware Graph Augmentation Learning0
Towards Better Dynamic Graph Learning: New Architecture and Unified LibraryCode2
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Fairness-Aware Graph Filter Design0
UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node ClassificationCode0
Graph Transformer GANs for Graph-Constrained House Generation0
GANN: Graph Alignment Neural Network for Semi-Supervised Learning0
Space-Invariant Projection in Streaming Network Embedding0
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network0
Exphormer: Sparse Transformers for GraphsCode1
Graph Positional Encoding via Random Feature Propagation0
Towards a GML-Enabled Knowledge Graph PlatformCode0
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks0
Framelet Message Passing0
A semantic backdoor attack against Graph Convolutional Networks0
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning ResearchCode1
Scalable Neural Network Training over Distributed GraphsCode0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification0
Graph Neural Networks with Learnable and Optimal Polynomial BasesCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Diffusion Probabilistic Models for Structured Node Classification0
Label Information Enhanced Fraud Detection against Low Homophily in GraphsCode1
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks0
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
Unnoticeable Backdoor Attacks on Graph Neural NetworksCode1
Self-Supervised Node Representation Learning via Node-to-Neighbourhood AlignmentCode1
On Generalized Degree Fairness in Graph Neural NetworksCode0
Heterophily-Aware Graph Attention Network0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks0
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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
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
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