20% fixed splits)

26개 결과 · ⬇ CSV · JSON

1:1 Accuracy

Rank	Model	1:1 Accuracy	Accuracy	Paper	Code	Year
1	NLGAT	88.5 ± 1.8	–	Non-Local Graph Neural Networks	divelab/Non-Local-GNN	2020
2	GCNII	88.37 ± 1.25	–	Simple and Deep Graph Convolutional Networks	chennnM/GCNII · chennnM/GCNII · zhanglab-aim/cancer-net · +1	2020
3	GloGNN++	88.33 ± 1.09	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
4	GloGNN	88.31 ± 1.13	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
5	ACMII-GCN++	88.25 ± 0.96	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
6	WRGAT	88.20 ± 2.26	–	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns	susheels/gnns-and-local-assortativity	2021
7	ACMII-GCN+	88.19 ± 1.17	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
8	ACM-GCN++	88.11 ± 0.96	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
9	NLGCN	88.1 ± 1.0	–	Non-Local Graph Neural Networks	divelab/Non-Local-GNN	2020
10	FAGCN	88.05 ± 1.57	–	Beyond Low-frequency Information in Graph Convolutional Networks	bdy9527/FAGCN	2021
11	ACM-GCN+	88.05 ± 0.99	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
12	ACMII-GCN	88.01 ± 1.08	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
13	GPRGCN	87.95 ± 1.18	–	Adaptive Universal Generalized PageRank Graph Neural Network	jianhao2016/GPRGNN	2020
14	GGCN	87.95 ± 1.05	–	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	yujun-yan/heterophily_and_oversmoothing	2021
15	H2GCN	87.87 ± 1.20	–	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs	GemsLab/H2GCN · GitEventhandler/H2GCN-PyTorch · sxwee/GNNsIMPL · +1	2020
16	ACM-SGC-2	87.69 ± 1.07	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
17	MixHop	87.61 ± 0.85	–	MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing	dmlc/dgl · benedekrozemberczki/MixHop-and-N-GCN · samihaija/mixhop	2019
18	Gen-NSD	87.30 ± 1.15	–	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	twitter-research/neural-sheaf-diffusion	2022
19	Diag-NSD	87.14 ± 1.06	–	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	twitter-research/neural-sheaf-diffusion	2022
20	ACM-SGC-1	86.9 ± 1.38	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022