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 17011750 of 1860 papers

TitleStatusHype
Topology Attack and Defense for Graph Neural Networks: An Optimization PerspectiveCode0
Attacking Graph Convolutional Networks via Rewiring0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional NetworksCode0
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples0
Exploiting Edge Features for Graph Neural Networks0
Graph Learning Network: A Structure Learning AlgorithmCode0
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs0
Graph Neural Networks Exponentially Lose Expressive Power for Node ClassificationCode0
Graph Attention Auto-EncodersCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Relation Structure-Aware Heterogeneous Information Network Embedding0
Graph U-NetsCode0
Network Representation Learning: Consolidation and Renewed BearingCode0
Few-shot Classification on Graphs with Structural Regularized GCNs0
CGNF: Conditional Graph Neural Fields0
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood MixingCode0
Graph Convolutional Networks with EigenPoolingCode0
Advancing GraphSAGE with A Data-Driven Node SamplingCode0
Robust Graph Data Learning via Latent Graph Convolutional Representation0
Exploring Structure-Adaptive Graph Learning for Robust Semi-Supervised Classification0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
Compositional Network Embedding0
Topological based classification of paper domains using graph convolutional networks0
Semi-Supervised Graph Classification: A Hierarchical Graph PerspectiveCode0
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks0
DeepGCNs: Can GCNs Go as Deep as CNNs?Code0
Quantum-based subgraph convolutional neural networks0
Learning Discrete Structures for Graph Neural NetworksCode0
Node Embedding over Temporal GraphsCode0
A Comparative Study for Unsupervised Network Representation Learning0
Fisher-Bures Adversary Graph Convolutional NetworksCode0
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method0
Fast Graph Representation Learning with PyTorch GeometricCode1
Gated Graph Convolutional Recurrent Neural NetworksCode1
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification0
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Batch Virtual Adversarial Training for Graph Convolutional Networks0
Adversarial Attacks on Graph Neural Networks via Meta LearningCode0
Graph Adversarial Training: Dynamically Regularizing Based on Graph StructureCode0
Simplifying Graph Convolutional NetworksCode1
Hierarchical Graph Convolutional Networks for Semi-supervised Node ClassificationCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Representation Learning for Heterogeneous Information Networks via Embedding EventsCode0
Hypergraph Convolution and Hypergraph Attention0
Network Lens: Node Classification in Topologically Heterogeneous Networks0
Search Efficient Binary Network EmbeddingCode0
Attributed Network Embedding via Subspace DiscoveryCode0
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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