Node Classification On Citeseer With Public

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
AIR-GCN	72.9%	GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction	-
LanczosNet	66.2 ± 1.9	LanczosNet: Multi-Scale Deep Graph Convolutional Networks	-
DSGCN	73.3	Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks	-
LinkDist	70.27%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
GRAND	75.4 ± 0.4	Graph Random Neural Network for Semi-Supervised Learning on Graphs	-
Snowball (tanh)	73.32%	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks	-
G-APPNP	72%	Pre-train and Learn: Preserve Global Information for Graph Neural Networks	-
IncepGCN+DropEdge	72.70%	-	-
CPF-tra-APPNP	74.6%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework	-
CoLinkDist	70.79%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
SSP	74.28 ± 0.67%	Optimization of Graph Neural Networks with Natural Gradient Descent	-
LDS-GNN	75.0%	Learning Discrete Structures for Graph Neural Networks	-
OGC	77.5	From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited	-
CoLinkDistMLP	70.96%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
SEGCN	73.4 ± 0.7	Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning	-
GAT	72.5 ± 0.7%	Graph Attention Networks	-
SuperGAT MX	72.6%	How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision	-
GraphMix(GCN)	74.52 ± 0.59	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	-
GraphSAGE	67.2	Inductive Representation Learning on Large Graphs	-
SSGC	73.6	Simple Spectral Graph Convolution

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