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 10011050 of 1860 papers

TitleStatusHype
Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns0
Matrix Manifold Neural Networks++0
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding0
Memory Augmented Design of Graph Neural Networks0
Message Detouring: A Simple Yet Effective Cycle Representation for Expressive Graph Learning0
Message Passing Neural Networks for Hypergraphs0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling0
Meta-Inductive Node Classification across Graphs0
Meta-multigraph Search: Rethinking Meta-structure on Heterogeneous Information Networks0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks0
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning0
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification0
Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)0
Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning0
Mixed-Curvature Transformers for Graph Representation Learning papersreview0
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification0
Mixture of Experts for Node Classification0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning0
Modeling Graph Node Correlations with Neighbor Mixture Models0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity0
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks0
Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks0
Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network0
Multi-Label Graph Convolutional Network Representation Learning0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING0
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings0
Multi-scale Graph Convolutional Networks with Self-Attention0
Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling0
Multi-task Self-distillation for Graph-based Semi-Supervised Learning0
Multivariate Relations Aggregation Learning in Social Networks0
Multi-view graph structure learning using subspace merging on Grassmann manifold0
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data0
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs0
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification0
NDGGNET-A Node Independent Gate based Graph Neural Networks0
Neighbor2vec: an efficient and effective method for Graph Embedding0
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks0
NEMR: Network Embedding on Metric of Relation0
Scalable Hypergraph Embedding System0
Network2Vec Learning Node Representation Based on Space Mapping in Networks0
Network In Graph Neural Network0
Network Lens: Node Classification in Topologically Heterogeneous Networks0
Network of Graph Convolutional Networks Trained on Random Walks0
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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
5GGCMAccuracy74.2Unverified
6GEMAccuracy74.2Unverified
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