Node Classification on genius

26개 결과 · ⬇ CSV · JSON

Accuracy

Rank	Model	Accuracy	1:1 Accuracy	Paper	Code	Year
1	Dual-Net GNN	91.45±0.11	–	Feature Selection: Key to Enhance Node Classification with Graph Neural Networks	sunilkmaurya/DualNetGNN_large	2023
2	ACM-GCN++	91.37 ± 0.07	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
3	ACM-GCN+	91.22 ± 0.13	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
4	ACMII-GCN+	91.13 ± 0.09	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
5	ACMII-GCN++	91.01 ± 0.18	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
6	GloGNN++	90.91 ± 0.13	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
7	G^2-GraphSAGE	90.85±0.64	–	Gradient Gating for Deep Multi-Rate Learning on Graphs	tk-rusch/gradientgating	2022
8	LINKX	90.77 ± 0.27	–	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
9	GloGNN	90.66 ± 0.11	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
10	MixHop	90.58 ± 0.16	–	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing	dmlc/dgl · benedekrozemberczki/MixHop-and-N-GCN · samihaija/mixhop	2019
11	GCNII	90.24 ± 0.09	–	Simple and Deep Graph Convolutional Networks	chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1	2020
12	GPRGCN	90.05 ± 0.31	–	Adaptive Universal Generalized PageRank Graph Neural Network	jianhao2016/GPRGNN	2020
13	GCNJK	89.30 ± 0.19	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
14	GCN	87.42 ± 0.37	–	Semi-Supervised Classification with Graph Convolutional Networks	dmlc/dgl · dmlc/dgl · tkipf/gcn · +52	2016
15	MLP	86.68 ± 0.09	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
16	APPNP	85.36 ± 0.62	–	Predict then Propagate: Graph Neural Networks meet Personalized PageRank	dmlc/dgl · dmlc/dgl · benedekrozemberczki/APPNP · +2	2018
17	C&S 2-hop	84.94 ± 0.49	–	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks	dmlc/dgl · sangyx/gtrick · CUAI/CorrectAndSmooth · +4	2020
18	C&S 1-hop	82.93 ± 0.15	–	Combining Label Propagation and Simple Models Out-performs Graph Neural Networks	dmlc/dgl · sangyx/gtrick · CUAI/CorrectAndSmooth · +4	2020
19	SGC 1-hop	82.36 ± 0.37	–	Simplifying Graph Convolutional Networks	dmlc/dgl · dmlc/dgl · dmlc/dgl · +4	2019
20	SGC 2-hop	82.10 ± 0.14	–	Simplifying Graph Convolutional Networks	dmlc/dgl · dmlc/dgl · dmlc/dgl · +4	2019