GENIE on these datasets as part of our future work.

TABLE E.1: Watermark verification performance (average of 10 runs) of GENIE
across 3 model architectures and 3 additional datasets.

Dataset | SEAL | GCN | GraphSAGE
---|---|---|---
$AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{clean}}$ (%) |  $AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{wm}}$ (%) |  $AUC_{\mathcal{D}_{wm}}^{\mathcal{M}_{wm}}$ (%) |  $AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{clean}}$ (%) |  $AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{wm}}$ (%) |  $AUC_{\mathcal{D}_{wm}}^{\mathcal{M}_{wm}}$ (%) |  $AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{clean}}$ (%) |  $AUC_{\mathcal{D}_{test}}^{\mathcal{M}_{wm}}$ (%) |  $AUC_{\mathcal{D}_{wm}}^{\mathcal{M}_{wm}}$ (%)
Router [70] | 95.68 | 95.86 | 96.22 | 96.75 | 96.53 | 95.23 | 92.85 | 96.27 | 95.44
ogbl-collab [68] | 95.56 | 95.17 | 99.92 | 96.39 | 100.00 | 95.71 | 96.94 | 100.00 | 95.79
Wikipedia [69] | 91.12 | 91.13 | 84.72 | 92.09 | 90.21 | 99.58 | 93.24 | 92.91 | 100.00