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ResearchFormerly WizWand

Graph Alignment on Sparse Erdős–Rényi graphs (average degree 4)

98AccuracyPROJ(DCX)
-1.8424.085075.92Oct 3, 2025
Updated 5mo ago

Evaluation results

MethodLinks
PROJ(DCX)pNOISE=02025.1098997
FAQ(DCX)pNOISE=02025.1098997
FAQ(DCX)pNOISE=0.052025.1098950
FGNN PROJpNOISE=02025.1098997
FGNN FAQpNOISE=02025.1098997
FGNN FAQpNOISE=0.052025.1098950
CHFGNN PROJpNOISE=02025.1098997
CHFGNN PROJpNOISE=0.052025.1098950
CHFGNN FAQpNOISE=02025.1098997
CHFGNN FAQpNOISE=0.052025.1098950
PROJ(DCX)pNOISE=0.052025.1097950
FAQ(DCX)pNOISE=0.12025.1096898
FGNN FAQpNOISE=0.12025.1096898
CHFGNN PROJpNOISE=0.12025.1096898
CHFGNN FAQpNOISE=0.12025.1096899
FAQ(DCX)pNOISE=0.152025.1095847
FGNN FAQpNOISE=0.152025.1095847
CHFGNN FAQpNOISE=0.152025.1095849
FGNN PROJpNOISE=0.052025.1094925
CHFGNN PROJpNOISE=0.152025.1094845
CHFGNN FAQpNOISE=0.22025.1093800
CHFGNN PROJpNOISE=0.22025.1091790
PROJ(DCX)pNOISE=0.12025.1090853
CHFGNN FAQpNOISE=0.252025.1085742
CHFGNN PROJpNOISE=0.252025.1082720
FGNN FAQpNOISE=0.22025.1081755
FGNN PROJpNOISE=0.12025.1074674
FAQ(DCX)pNOISE=0.22025.1073723
PROJ(DCX)pNOISE=0.152025.1059499
CHFGNN FAQpNOISE=0.32025.1052638
CHFGNN PROJpNOISE=0.32025.1049549
FGNN PROJpNOISE=0.152025.1044365
FGNN FAQpNOISE=0.252025.1024535
PROJ(DCX)pNOISE=0.22025.1023195
FGNN PROJpNOISE=0.22025.1023193
FAQ(DCX)pNOISE=0.252025.1013504
FGNN PROJpNOISE=0.252025.1012134
PROJ(DCX)pNOISE=0.252025.109130
CHFGNN FAQpNOISE=0.352025.109546
CHFGNN PROJpNOISE=0.352025.108367
FGNN FAQpNOISE=0.32025.107494
FGNN PROJpNOISE=0.32025.106114
PROJ(DCX)pNOISE=0.32025.104115
FAQ(DCX)pNOISE=0.32025.104487
FGNN PROJpNOISE=0.352025.103103
FGNN FAQpNOISE=0.352025.103485
PROJ(DCX)pNOISE=0.352025.102112
FAQ(DCX)pNOISE=0.352025.102485