Node Classification on Aggregated fairness datasets benchmark leaderboard · SOTA2 Research

Node Classification on Aggregated fairness datasets

71.44AccuracyRej. Opt.

Evaluation Results

Method	Links
Rej. Opt.2026.06		71.44	8.85	30.59
Reweighting2026.06		70.3	5.2	30.52
Diag-FSF2026.06		70.18	5.99	30.68
XGBoost2026.06		70.03	9.24	31.85
LightGBM2026.06		69.35	8.63	32.52
Fair Drop2026.06		68.31	9.07	34.41
Diag-FSP2026.06		68.2	7.44	33.74
FairGNN2026.06		67.96	5.2	32.77
Gen-FVP2026.06		67.75	17.1	39.34
Gen-FSF2026.06		67.69	9.16	33.78
Gen-FSP2026.06		67.53	4.69	33
MLP2026.06		67.44	8.47	34.99
adv-MLP2026.06		67.3	12.63	36.15
H2GCN2026.06		67.04	7.5	34.53
Diag-FVP2026.06		66.91	5.1	34.1
Undersampling2026.06		66.75	3.73	33.61
FairSIN2026.06		66.48	8.52	36.22
GCN2026.06		66.36	8.57	36.21
Log. Reg.2026.06		66.26	11.51	36.78
Gen-NSD2026.06		66.1	10.41	37.15
O(d)-FSF2026.06		63.39	9.07	39.69
O(d)-FSP2026.06		62.58	5.54	38.48
O(d)-NSD2026.06		62.26	12.75	43.09
NIFTY2026.06		59.94	10.09	43.11
O(d)-FVP2026.06		59.4	10.99	43.16
Diag-NSD2026.06		58.48	11.42	45.3
GAT2026.06		57.79	11.99	46.72
GraphSAGE2026.06		57.78	12.2	47.29