Node Classification on Texas

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	MGNN + Hetero-S (8 layers)	93.09	No	The Heterophilic Snowflake Hypothesis: Training ...	2024-06-18	Code
2	FDGATII	80.5405	No	FDGATII : Fast Dynamic Graph Attention with Init...	2021-10-21	Code
3	Geom-GCN-P	67.57	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code
4	Geom-GCN-S	59.73	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code
5	Geom-GCN-I	57.58	No	Geom-GCN: Geometric Graph Convolutional Networks	2020-02-13	Code

#1MGNN + Hetero-S (8 layers)SOTA
93.09
Accuracy· 2024-06-18
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs Code
#2FDGATIISOTA
80.5405
Accuracy· 2021-10-21
FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping Code
#3Geom-GCN-PSOTA
67.57
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code
#4Geom-GCN-S
59.73
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code
#5Geom-GCN-I
57.58
Accuracy· 2020-02-13
Geom-GCN: Geometric Graph Convolutional Networks Code