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

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Showing 1101–1150 of 1860 papers

TitleStatusHype
Uplifting Message Passing Neural Network with Graph Original Information—0
Bridging Graph Network to Lifelong Learning with Feature Interaction—0
A Statistical Relational Approach to Learning Distance-based GCNs—0
Bridging Source and Target Domains via Link Prediction for Unsupervised Domain Adaptation on Graphs—0
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
Bures-Wasserstein Means of Graphs—0
Cached Operator Reordering: A Unified View for Fast GNN Training—0
Cadence Detection in Symbolic Classical Music using Graph Neural Networks—0
Can we Soft Prompt LLMs for Graph Learning Tasks?—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Causally Fair Node Classification on Non-IID Graph Data—0
Causally-guided Regularization of Graph Attention Improves Generalizability—0
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks—0
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation—0
CGNF: Conditional Graph Neural Fields—0
Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain—0
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network—0
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
CLEAR: Cluster-based Prompt Learning on Heterogeneous Graphs—0
CN-Motifs Perceptive Graph Neural Networks—0
Co-embedding of Nodes and Edges with Graph Neural Networks—0
ColdExpand: Semi-Supervised Graph Learning in Cold Start—0
Collective Robustness Certificates—0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks—0
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks—0
Communication-Free Distributed GNN Training with Vertex Cut—0
Community detection in networks using graph embeddings—0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks—0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks—0
Compositional Network Embedding—0
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks—0
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs—0
Conditional Local Feature Encoding for Graph Neural Networks—0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
Conformal Inductive Graph Neural Networks—0
Connecting Graph Convolutional Networks and Graph-Regularized PCA—0
Connecting Graph Convolution and Graph PCA—0
Constant Curvature Graph Convolutional Networks—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE—0
Contrastive Disentangled Learning on Graph for Node Classification—0
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels—0
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs—0
Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification—0
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All datasetsCiteseerPubmedCoraCiteSeer with Public Split: fixed 20 nodes per classPubMed with Public Split: fixed 20 nodes per classCora with Public Split: fixed 20 nodes per classPPICoauthor CSCora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09—Unverified
2SplineCNNAccuracy79.2—Unverified
3PathNetAccuracy (%)77.98—Unverified
43ferenceAccuracy76.33—Unverified
5MMAAccuracy76.3—Unverified
6PPNPAccuracy75.83—Unverified
7CoLinkDistAccuracy75.79—Unverified
8CoLinkDistMLPAccuracy75.77—Unverified
9APPNPAccuracy75.73—Unverified
10CleoraAccuracy75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21—Unverified
2CoLinkDistAccuracy89.58—Unverified
3CoLinkDistMLPAccuracy89.53—Unverified
4PathNetAccuracy (%)88.92—Unverified
53ferenceAccuracy88.9—Unverified
6SplineCNNAccuracy88.88—Unverified
7LinkDistAccuracy88.86—Unverified
8LinkDistMLPAccuracy88.79—Unverified
9PairEF188.57—Unverified
10GCN + MixupAccuracy87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24—Unverified
2CoLinkDistAccuracy87.89—Unverified
33ferenceAccuracy87.78—Unverified
4LinkDistMLPAccuracy87.58—Unverified
5CoLinkDistMLPAccuracy87.54—Unverified
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10DifNetAccuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GGCMAccuracy74.2—Unverified
6GEMAccuracy74.2—Unverified
7Truncated KrylovAccuracy73.86—Unverified
8SSGCAccuracy73.6—Unverified
9OKDEEMAccuracy73.53—Unverified
10GCNIIAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4—Unverified
2CPF-tra-GCNIIAccuracy83.2—Unverified
3DSGCNAccuracy81.9—Unverified
4Truncated KrylovAccuracy81.7—Unverified
5SuperGAT MXAccuracy81.7—Unverified
6G-APPNPAccuracy80.95—Unverified
7GGCMAccuracy80.8—Unverified
8GCN(predicted-targets)Accuracy80.42—Unverified
9SSGCAccuracy80.4—Unverified
10GCNIIAccuracy80.2—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9—Unverified
2GCN-TVAccuracy86.3—Unverified
3GCNIIAccuracy85.5—Unverified
4CPF-ind-APPNPAccuracy85.3—Unverified
5AIR-GCNAccuracy84.7—Unverified
6H-GCNAccuracy84.5—Unverified
7G-APPNPAccuracy84.31—Unverified
8SuperGAT MXAccuracy84.3—Unverified
9DSGCNAccuracy84.2—Unverified
10LDS-GNNAccuracy84.1—Unverified