Node Classification on Citeseer

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Accuracy

Rank	Model	Accuracy	Training Split	Validation	1:1 Accuracy	Accuracy (%)	Inference Time (ms)	Extra Training Data	Paper	Code	Year
1	ACMII-Snowball-2	82.07 ± 1.04	–	–	–	–	–		Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
2	ACM-GCN	81.68 ± 0.97	–	–	–	–	–		Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
3	ACM-Snowball-2	81.58 ± 1.23	–	–	–	–	–		Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
4	ACMII-Snowball-3	81.56 ± 1.15	–	–	–	–	–		Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?		2021
5	SSP	80.52 ± 0.14	–	–	–	–	–		Optimization of Graph Neural Networks with Natural Gradient Descent	russellizadi/ssp	2020
6	NodeNet	80.09%	–	–	–	–	–		NodeNet: A Graph Regularised Neural Network for Node Classification		2020
7	SplineCNN	79.20%	–	–	–	–	–		SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels	rusty1s/pytorch_geometric · rusty1s/pytorch_spline_conv · abhilash1910/SpectralEmbeddings · +2	2017
8	GCN-LPA	78.7 ± 0.6	–	–	–	–	–		Unifying Graph Convolutional Neural Networks and Label Propagation	hwwang55/GCN-LPA · achalagarwal/gcn-lpa	2020
9	Graph-MLP + SWA	77.99 ± 1.57%	–	–	–	–	–		The Split Matters: Flat Minima Methods for Improving the Performance of GNNs	foisunt/fmms-in-gnns	2023
10	CNMPGNN	76.81±1.40	–	–	–	–	–		CN-Motifs Perceptive Graph Neural Networks		2021
11	CGT	76.59±0.98	–	–	–	–	–		Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures	nslab-cuk/community-aware-graph-transformer	2023
12	3ference	76.33	–	–	–	–	–		Inferring from References with Differences for Semi-Supervised Node Classification on Graphs	cf020031308/3ference	2022
13	MMA	76.30%	–	–	–	–	–		Multi-Mask Aggregators for Graph Neural Networks	asarigun/mma	2022
14	AdaGCN	76.22 ± 0.20	–	–	–	–	–	✓	AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models	datake/AdaGCN	2019
15	UGT	76.08±2.5	–	–	–	–	–		Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity	nslab-cuk/unified-graph-transformer · nslab-cuk/community-aware-graph-transformer · nslab-cuk/literalkg	2023
16	PPNP	75.83%	–	YES	–	–	–	✓	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	dmlc/dgl · dmlc/dgl · benedekrozemberczki/APPNP · +2	2018
17	CoLinkDist	75.79%	–	–	–	–	–		Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
18	CoLinkDistMLP	75.77%	–	–	–	–	–		Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	cf020031308/LinkDist · cf020031308/LinkDist	2021
19	APPNP	75.73%	–	–	–	–	–	✓	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	dmlc/dgl · dmlc/dgl · benedekrozemberczki/APPNP · +2	2018
20	Cleora	75.7	–	–	–	–	–		Cleora: A Simple, Strong and Scalable Graph Embedding Scheme	Synerise/cleora · Synerise/booking-challenge	2021