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 12011225 of 1860 papers

TitleStatusHype
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness0
Dual Node and Edge Fairness-Aware Graph Partition0
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs0
Node-Time Conditional Prompt Learning In Dynamic Graphs0
DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks0
Dynamic Graph Embedding via LSTM History Tracking0
Dynamic Graph Node Classification via Time Augmentation0
Dynamic Joint Variational Graph Autoencoders0
Dynamic Spiking Framework for Graph Neural Networks0
Dynamic Stacked Generalization for Node Classification on Networks0
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression0
Edge2Node: Reducing Edge Prediction to Node Classification0
Edge: Enriching Knowledge Graph Embeddings with External Text0
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification0
Edge-Featured Graph Attention Network0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning0
Initialization for Network Embedding: A Graph Partition Approach0
Effective Polynomial Filter Adaptation for Graph Neural Networks0
Effects of Graph Convolutions in Multi-layer Networks0
Efficient Ensembles of Graph Neural Networks0
Efficient Learning on Large Graphs using a Densifying Regularity Lemma0
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs0
_2-norm Flow Diffusion in Near-Linear Time0
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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
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