Node Classification on Cornell

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	FDGATII	82.4324	No	FDGATII : Fast Dynamic Graph Attention with Init...	2021-10-21	Code
2	CT-Layer	69.04	No	DiffWire: Inductive Graph Rewiring via the Lovás...	2022-06-15	Code
3	MGNN + Hetero-S (4 layers)	68.18	No	The Heterophilic Snowflake Hypothesis: Training ...	2024-06-18	Code
4	Geom-GCN-P	60.81	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code
5	CT-Layer (PE)	58.02	No	DiffWire: Inductive Graph Rewiring via the Lovás...	2022-06-15	Code
6	Geom-GCN-I	56.76	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code
7	Geom-GCN-S	55.68	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code

#1FDGATIISOTA
82.4324
Accuracy· 2021-10-21
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping Code
#2CT-Layer
69.04
Accuracy· 2022-06-15
DiffWire: Inductive Graph Rewiring via the Lovász Bound Code
#3MGNN + Hetero-S (4 layers)
68.18
Accuracy· 2024-06-18
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs Code
#4Geom-GCN-PSOTA
60.81
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code
#5CT-Layer (PE)
58.02
Accuracy· 2022-06-15
DiffWire: Inductive Graph Rewiring via the Lovász Bound Code
#6Geom-GCN-I
56.76
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code
#7Geom-GCN-S
55.68
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code