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 12511300 of 1860 papers

TitleStatusHype
Graph Sanitation with Application to Node Classification0
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional NetworksCode1
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective0
Improving Graph Neural Networks with Simple Architecture DesignCode1
Neural Trees for Learning on Graphs0
Meta-Inductive Node Classification across Graphs0
GIPA: General Information Propagation Algorithm for Graph LearningCode1
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs0
Accelerating Large Scale Real-Time GNN Inference using Channel PruningCode1
Graph Attention Networks with Positional Embeddings0
Non-Recursive Graph Convolutional Networks0
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
CrossWalk: Fairness-enhanced Node Representation LearningCode1
VersaGNN: a Versatile accelerator for Graph neural networks0
WGCN: Graph Convolutional Networks with Weighted Structural FeaturesCode1
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification0
LGD-GCN: Local and Global Disentangled Graph Convolutional NetworksCode1
Seastar: vertex-centric programming for graph neural networks0
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud DetectionCode1
SAS: A Simple, Accurate and Scalable Node Classification Algorithm0
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural NetworksCode1
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural NetworksCode1
A Hyperbolic-to-Hyperbolic Graph Convolutional Network0
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood0
Edge: Enriching Knowledge Graph Embeddings with External Text0
Explainability-based Backdoor Attacks Against Graph Neural Networks0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
New Benchmarks for Learning on Non-Homophilous GraphsCode1
Modeling Graph Node Correlations with Neighbor Mixture Models0
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs0
Bag of Tricks for Node Classification with Graph Neural NetworksCode1
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link PredictionCode0
OGB-LSC: A Large-Scale Challenge for Machine Learning on GraphsCode1
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural NetworksCode1
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning0
Graph Neural Networks Inspired by Classical Iterative AlgorithmsCode1
Scalable Hypergraph Embedding System0
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks0
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural NetworksCode1
Learning Graph Neural Networks with Positive and Unlabeled Nodes0
Unified Robust Training for Graph NeuralNetworks against Label Noise0
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphsCode0
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue GraphsCode1
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation FrameworkCode1
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance ReductionCode0
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-LearningCode1
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced DataCode1
Graphfool: Targeted Label Adversarial Attack on Graph Embedding0
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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
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
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