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 901950 of 1860 papers

TitleStatusHype
Hub-aware Random Walk Graph Embedding Methods for Classification0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach0
Hyperbolic Heterogeneous Graph Attention Networks0
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views0
Hypergraph-Based Dynamic Graph Node Classification0
Hypergraph Convolution and Hypergraph Attention0
Training-Free Message Passing for Learning on Hypergraphs0
HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network0
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach0
ID-MixGCL: Identity Mixup for Graph Contrastive Learning0
I-GCN: Robust Graph Convolutional Network via Influence Mechanism0
Imbalanced Node Classification Beyond Homophilic Assumption0
Imbalanced Node Processing Method in Graph Neural Network Classification Task0
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification0
IMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs0
Implicit Kernel Attention0
Implicit vs Unfolded Graph Neural Networks0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization0
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network0
Improving Node Representation by Boosting Target-Aware Contrastive Loss0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling0
Incremental Learning on Growing Graphs0
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks0
Inductive Graph Embeddings through Locality Encodings0
Inductive Graph Few-shot Class Incremental Learning0
Inductive Linear Probing for Few-shot Node Classification0
Inductive Lottery Ticket Learning for Graph Neural Networks0
Infant Cry Classification with Graph Convolutional Networks0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs0
Informative Pseudo-Labeling for Graph Neural Networks with Few Labels0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning0
InstantEmbedding: Efficient Local Node Representations0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification0
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method0
Investigating Extensions to Random Walk Based Graph Embedding0
Investigating Transfer Learning in Graph Neural Networks0
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?0
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?0
Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?0
Is Homophily a Necessity for Graph Neural Networks?0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning0
Iterated graph neural network system0
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach0
Joint Use of Node Attributes and Proximity for Semi-Supervised Classification on Graphs0
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks0
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural 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