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Node Classification On Citeseer

Metrics

Accuracy
Validation

Results

Performance results of various models on this benchmark

Paper Title
ACMII-Snowball-282.07 ± 1.04-Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
ACM-GCN81.68 ± 0.97-Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
ACM-Snowball-281.58 ± 1.23-Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
ACMII-Snowball-381.56 ± 1.15-Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
SSP80.52 ± 0.14-Optimization of Graph Neural Networks with Natural Gradient Descent
NodeNet80.09%-NodeNet: A Graph Regularised Neural Network for Node Classification
SplineCNN79.20%-SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
GCN-LPA78.7 ± 0.6-Unifying Graph Convolutional Neural Networks and Label Propagation
Graph-MLP + SWA77.99 ± 1.57%-The Split Matters: Flat Minima Methods for Improving the Performance of GNNs
CNMPGNN76.81±1.40-CN-Motifs Perceptive Graph Neural Networks
CGT76.59±0.98-Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
3ference76.33-Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
MMA76.30%-Multi-Mask Aggregators for Graph Neural Networks
AdaGCN76.22 ± 0.20-AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models
UGT76.08±2.5-Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
PPNP75.83%YESPredict then Propagate: Graph Neural Networks meet Personalized PageRank
CoLinkDist75.79%-Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDistMLP75.77%-Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
APPNP75.73%-Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Cleora75.7-Cleora: A Simple, Strong and Scalable Graph Embedding Scheme
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