Node Classification on Pubmed

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Accuracy

Rank	Model	Accuracy	Training Split	F1	Validation	Accuracy (%)	F1-Score	Inference Time (ms)	Paper	Code	Year
1	NCGCN	91.64 ± 0.53	–	–	–	–	–	–	Clarify Confused Nodes via Separated Learning	GISec-Team/NCGNN	2023
2	NCSAGE	91.55 ± 0.38	–	–	–	–	–	–	Clarify Confused Nodes via Separated Learning	GISec-Team/NCGNN	2023
3	ACMII-Snowball-3	91.31 ± 0.6	–	–	–	–	–	–	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
4	ACM-GCN	90.74 ± 0.5	–	–	–	–	–	–	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
5	Graph-MLP + SAF	90.64 ± 0.46%	–	–	–	–	–	–	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs	foisunt/fmms-in-gnns	2023
6	ACMII-Snowball-2	90.56 ± 0.39	–	–	–	–	–	–	Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
7	NodeNet	90.21%	–	–	–	–	–	–	NodeNet: A Graph Regularised Neural Network for Node Classification		2020
8	CNMPGNN	90.07± 0.43	–	–	–	–	–	–	CN-Motifs Perceptive Graph Neural Networks		2021
9	CoLinkDist	89.58%	–	–	–	–	–	–	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
10	CoLinkDistMLP	89.53%	–	–	–	–	–	–	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
11	SSP	89.36 ± 0.57	–	–	–	–	–	–	Optimization of Graph Neural Networks with Natural Gradient Descent	russellizadi/ssp	2020
12	3ference	88.90	–	–	–	–	–	–	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	cf020031308/3ference	2022
13	SplineCNN	88.88%	–	–	–	–	–	–	SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels	rusty1s/pytorch_geometric · rusty1s/pytorch_spline_conv · abhilash1910/SpectralEmbeddings · +2	2017
14	LinkDist	88.86%	–	–	–	–	–	–	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
15	LinkDistMLP	88.79%	–	–	–	–	–	–	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
16	GCN + Mixup	87.9%	–	–	–	–	–	–	Mixup for Node and Graph Classification	vanoracai/MixupForGraph	2021
17	GCN-LPA	87.8 ± 0.6	–	–	–	–	–	–	Unifying Graph Convolutional Neural Networks and Label Propagation	hwwang55/GCN-LPA · achalagarwal/gcn-lpa	2020
18	CGT	86.86±0.12	–	–	–	–	–	–	Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures	nslab-cuk/community-aware-graph-transformer	2023
19	GRACE	86.7 ± 0.1	–	–	–	–	–	–	Deep Graph Contrastive Representation Learning	dmlc/dgl · CRIPAC-DIG/GRACE · ycremar/DIG-SSL	2020
20	CT-Layer (PE)	86.07	–	–	–	–	–	–	DiffWire: Inductive Graph Rewiring via the Lovász Bound	ellisalicante/GraphRewiring-Tutorial · AdrianArnaiz/DiffWire	2022