Node Classification on Penn94

32개 결과 · ⬇ CSV · JSON

Accuracy

Rank	Model	Accuracy	Paper	Code	Year
1	Dual-Net GNN	86.09±0.56	Feature Selection: Key to Enhance Node Classification with Graph Neural Networks	sunilkmaurya/DualNetGNN_large	2023
2	ACM-GCN++	86.08 ± 0.43	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
3	ACMII-GCN++	85.95 ± 0.26	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
4	GloGNN++	85.74±0.42	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
5	GloGNN	85.57 ± 0.35	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
6	GNNMoE(GCN-like P)	85.11±0.39	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	GISec-Team/GNNMoE	2024
7	ACM-GCN+	85.05 ± 0.19	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
8	ACMII-GCN+	84.95 ± 0.43	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
9	DJ-GNN	84.84±0.34	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	AhmedBegggaUA/TFM	2023
10	NCGCN	84.74 ± 0.28	Clarify Confused Nodes via Separated Learning	GISec-Team/NCGNN	2023
11	LINKX	84.71 ± 0.52	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods	cuai/non-homophily-large-scale · CUAI/Non-Homophily-Benchmarks · ivam-he/chebnetii · +2	2021
12	GNNMoE(SAGE-like P)	84.05±0.37	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	GISec-Team/GNNMoE	2024
13	MixHop	83.47 ± 0.71	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing	dmlc/dgl · benedekrozemberczki/MixHop-and-N-GCN · samihaija/mixhop	2019
14	GCNII	82.92 ± 0.59	Simple and Deep Graph Convolutional Networks	chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1	2020
15	GCN	82.47 ± 0.27	Semi-Supervised Classification with Graph Convolutional Networks	dmlc/dgl · dmlc/dgl · tkipf/gcn · +52	2016
16	GNNMoE(GAT-like P)	81.98±0.47	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	GISec-Team/GNNMoE	2024
17	NCSAGE	81.77 ± 0.71	Clarify Confused Nodes via Separated Learning	GISec-Team/NCGNN	2023
18	GCNJK	81.63 ± 0.54	New Benchmarks for Learning on Non-Homophilous Graphs	CUAI/Non-Homophily-Benchmarks	2021
19	GAT	81.53 ± 0.55	Graph Attention Networks	labmlai/annotated_deep_learning_paper_implementations · dmlc/dgl · dmlc/dgl · +90	2017
20	GPRGCN	81.38 ± 0.16	Adaptive Universal Generalized PageRank Graph Neural Network	jianhao2016/GPRGNN	2020