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 14011450 of 1860 papers

TitleStatusHype
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled GraphsCode0
On Local Aggregation in Heterophilic Graphs0
Graph Belief Propagation NetworksCode0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data0
Embedding Knowledge Graphs Attentive to Positional and Centrality QualitiesCode0
_2-norm Flow Diffusion in Near-Linear Time0
Relational Graph Neural Network Design via Progressive Neural Architecture Search0
GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily0
Local, global and scale-dependent node rolesCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification0
Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative PositionsCode0
A Robust and Generalized Framework for Adversarial Graph EmbeddingCode0
Auxiliary learning induced graph convolutional networks0
Ultrahyperbolic Neural Networks0
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?0
Free Energy Node Embedding via Generalized Skip-gram with Negative SamplingCode0
Graph Sanitation with Application to Node Classification0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective0
Neural Trees for Learning on Graphs0
Meta-Inductive Node Classification across Graphs0
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs0
Non-Recursive Graph Convolutional Networks0
Graph Attention Networks with Positional Embeddings0
VersaGNN: a Versatile accelerator for Graph neural networks0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification0
Seastar: vertex-centric programming for graph neural networks0
SAS: A Simple, Accurate and Scalable Node Classification Algorithm0
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
Modeling Graph Node Correlations with Neighbor Mixture Models0
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs0
Deepened Graph Auto-Encoders Help Stabilize and Enhance Link PredictionCode0
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning0
2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks0
Scalable Hypergraph Embedding System0
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
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance ReductionCode0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding0
Generalized Equivariance and Preferential Labeling for GNN Node ClassificationCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
A Statistical Relational Approach to Learning Distance-based GCNs0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes0
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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