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 15011550 of 1860 papers

TitleStatusHype
Unsupervised Adversarially-Robust Representation Learning on Graphs0
A graph similarity for deep learning0
Adversarial Attacks on Deep Graph Matching0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models0
Cyclic Label Propagation for Graph Semi-supervised Learning0
AutoGraph: Automated Graph Neural Network0
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks0
Revisiting graph neural networks and distance encoding from a practical viewCode0
Adversarial Context Aware Network Embeddings for Textual Networks0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs0
GAGE: Geometry Preserving Attributed Graph Embeddings0
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network0
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational InferenceCode0
On the Impact of Communities on Semi-supervised Classification Using Graph Neural NetworksCode0
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation LearningCode0
Geometric Scattering Attention NetworksCode0
Deperturbation of Online Social Networks via Bayesian Label TransitionCode0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks0
Co-embedding of Nodes and Edges with Graph Neural Networks0
Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs0
Anisotropic Graph Convolutional Network for Semi-supervised Learning0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks0
InstantEmbedding: Efficient Local Node Representations0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models0
NodeSig: Binary Node Embeddings via Random Walk Diffusion0
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks0
Spectral Embedding of Graph Networks0
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side InformationCode0
Framework for Designing Filters of Spectral Graph Convolutional Neural Networks in the Context of Regularization TheoryCode0
Revisiting Graph Neural Networks for Link Prediction0
PanRep: Universal node embeddings for heterogeneous graphs0
Graph Joint Attention Networks0
Inductive Graph Embeddings through Locality Encodings0
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective0
Streaming Graph Neural Networks via Continual Learning0
Layer-stacked Attention for Heterogeneous Network Embedding0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Community detection in networks using graph embeddings0
CatGCN: Graph Convolutional Networks with Categorical Node FeaturesCode0
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge GraphsCode0
Permutation-equivariant and Proximity-aware Graph Neural Networks with Stochastic Message PassingCode0
LFGCN: Levitating over Graphs with Levy Flights0
SAIL: Self-Augmented Graph Contrastive Learning0
Beyond Observed Connections : Link InjectionCode0
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence FunctionCode0
Decoupled Variational Embedding for Signed Directed NetworksCode0
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems0
Learning Node Representations against PerturbationsCode0
Tackling Over-Smoothing for General Graph Convolutional Networks0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional 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
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