Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training practices such as specifically weight initialisation and normalisation that remain underexplored. We present a systematic ablation of initialisation and normalisation strategies across three GNN architectures (GCN, GAT, and GraphSAGE) on the Elliptic Bitcoin dataset. Our experiments reveal that initialisation and normalisation are architecture-dependent: GraphSAGE achieves the strongest performance with Xavier initialisation alone, GAT benefits most from combining GraphNorm with Xavier initialisation, while GCN shows limited sensitivity to these modifications. These findings offer practical, architecture-specific guidance for deploying GNNs in AML pipelines for datasets with severe class imbalance. We release a reproducible experimental framework with temporal data splits, seeded runs, and full ablation results.
Figures & tables
Figure 1: The base model architecture. The evaluated architecture follows a modular design consisting of an input layer (166 transaction features), followed by 1–3 GNN layers with 3 different types (GCN, GAT, and GraphSAGE). Each GNN variant is followed by GraphNorm and dropout (0.08–0.64) for regularisation. The processed features are passed to an embedding layer (64-128 dimensions) and then to a classification head (linear layer) for binary prediction. All layers use standard weight initialisation methods.
Parameter
Range
Scale
GAT
GCN
GraphSAGE
Learning rate
[2×10−6,10−3]
Log
6.999×10−4
8.475×10−4
5.302×10−4
Hidden dimension
[128,256]
Linear
148
211
140
Embedding dim.
[64,128]
Linear
89
90
103
Number of layers
{1,2,3}
Categorical
2
2
2
Dropout rate
[0.08,0.64]
Log
0.2522
0.2361
0.1135
Number of epochs
[128,512]
Linear
508
497
397
Table 1: Hyperparameter search space and optimal values obtained for each model via Optuna optimisation. Weight decay is fixed at 5×10−4 for all models.
Baseline
Xavier
GraphNorm + Xavier
Model
AUC
AUPRC
AUC
AUPRC
888 AUC
AUPRC
GCN
0.8728
0.5993
0.8740
0.5939
888 0.8736
0.5442
GAT
0.8585
0.6022
0.8486
0.6190
888 0.8700
0.6568
GraphSAGE
0.8593
0.6551
0.8826
0.6678
888 0.8755
0.6651
Table 2: Effect of initialisation and GraphNorm on model performance. GCN achieves its best AUPRC at baseline (0.5993), with marginal changes under Xavier or GraphNorm. GAT benefits most from GraphNorm combined with Xavier ( + 0.055 AUPRC, + 0.012 AUC over baseline). GraphSAGE reaches peak performance with Xavier initialisation alone ( + 0.013 AUPRC, + 0.023 AUC over baseline), with GraphNorm providing diminishing returns.
Figure 2: Convergence behaviour of GCN, GAT, and GraphSAGE on the Elliptic dataset. ( 2(a) ) Training loss curves show that GraphSAGE converges faster than GCN and GAT. ( 2(b) ) Validation score trajectories highlight that GraphSAGE consistently achieves higher validation scores, while GAT exhibits more variance and GCN stabilises more slowly.
Figure 3: Performance comparison of GCN, GAT, and GraphSAGE on the Elliptic dataset. ( 3(a) ) Overall performance across metrics including AUC, AUPRC, and F1 at multiple thresholds. ( 3(b) ) Precision-Recall scatter plots at 90% confidence, illustrating the distribution across architectures.
Method
AUC
AUPRC
Source
Prior GNN Methods
GraphSAGE
0.8712
0.6392
Deprez et al. [ 7 ]
This Work
GAT (GraphNorm + Xavier)
0.8700
0.6568
Ours
GCN (baseline)
0.8728
0.5993
Ours
GraphSAGE (Xavier)
0.8826
0.6678
Ours
Table 3: Comparison with prior methods on the Elliptic dataset. Direct comparison is limited by differences in evaluation protocols.