A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
Authors: Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer, Gonzalo Wandosell Fernández de Bobadilla
Organizations: Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain · Institute of IT Management and Digitization Research (IFID), FOM University of Applied Sciences in Economics and Management, Düsseldorf, Germany
While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains largely unexplored. Consequently, it remains unclear which signals provide consistent and robust improvements. This paper presents a taxonomy-driven empirical analysis of graph-derived signals for tabular machine learning. We propose a unified and reproducible evaluation protocol to systematically assess which categories of graph-derived signals yield statistically significant and robust performance improvements. The protocol provides an extensible setup for the controlled integration of diverse graph-derived signals into tabular learning pipelines. To ensure a fair and rigorous comparison, it incorporates automated hyperparameter optimization, multi-seed statistical evaluation, formal significance testing, and robustness analysis under graph perturbations. We demonstrate the protocol through an extensive case study on a large-scale, imbalanced cryptocurrency fraud detection dataset. The analysis identifies signal categories providing consistently reliable performance gains and offers interpretable insights into which graph-derived signals indicate fraud-discriminative structural patterns. Furthermore, robustness analyses reveal pronounced differences in how various signals handle missing or corrupted relational data. These findings demonstrate practical utility for fraud detection and illustrate how the proposed taxonomy-driven evaluation protocol can be applied in other application domains.
Figures & tables
Category
Method
Description
Centrality
Degree Centrality
Number of incident edges
PageRank
Recursive importance scoring
Betweenness Centrality
Fraction of shortest paths through node
Eigenvector Centrality
Importance based on neighbors’ importance
Closeness Centrality
Inverse average shortest path length
Cohesion
Clustering Coefficient
Density of direct neighbors
Table 1: Representative graph signals categorized by taxonomic family.
Figure 1: Average F1 -score improvements across graph signal categories relative to the transaction-only baseline, aggregated over classifiers and random seeds (trimmed aggregation).
Figure 2: Per-classifier F1 -scores across graph signal categories. Cell values report mean F1 -scores with standard deviations across random seeds. Color intensity indicates relative performance differences with respect to the transaction-only (TRX) baseline (green = improvement, red = degradation).
Figure 3: Aggregated McNemar test outcomes for graph signal categories across classifiers. Each cell represents the balance of statistically significant improvements ( p≤0.05 ) versus degradations. Darker green shades indicate a higher frequency of significant performance gains over the transaction-only baseline.
Figure 4: Average ΔF1 relative to the transaction-only baseline across graph signal categories under increasing edge removal.
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Mean F1 -scores across classifiers and graph signals. Cell values report the mean F1 -score aggregated over the trimmed middle eight runs. Color intensity indicates relative performance differences with respect to the transaction-only baseline (green = improvement, red = degradation).
Figure 6: Standard deviation of F1 -scores across random seeds. Values report the standard deviation of F1 -scores over the trimmed middle eight runs. Darker shading indicates higher variability. Graph signals are grouped by category.
Figure 7: Signal-level aggregation of McNemar test outcomes across classifiers and runs ( p≤0.05 ). Cells show the number of statistically significant improvements versus degradations (#better / #worse) for each classifier–graph signal combination, aggregated over ten random seeds. Color intensity reflects the net balance between improvements and degradations.
Figure 8: Performance comparison under structural graph perturbations with 0% edge removal (transaction-only reference). Cell values report mean F1 -scores with standard deviations across random seeds.
Figure 9: Performance comparison under moderate structural degradation with 25 % random edge removal.
Figure 10: Performance comparison under stronger structural degradation with 50 % random edge removal.
Figure 11: Classifier-specific robustness trends illustrating how average ΔF1 improvements relative to the transaction-only baseline evolve under increasing levels of random edge removal (0%, 25%, 50%) for different graph signal categories.
Model
Parameter
Search Space
Logistic Regression
penalty
{l2,None}
C
log-uniform [0.001,10]
solver
{lbfgs,saga}
balancing_strategy
{none,oversampling,class_weights}
use_emb_pca
{False,True}
emb_pca_n
{16,32,48,64,0.95}
Appendix
Table 2: Hyperparameter search spaces for all evaluated classifiers. Only the parameters listed are optimized via Bayesian optimization (Hyperopt) with a fixed trial budget; all remaining parameters are kept at library default values.
Approximate computation for large graphs; sampled nodes: min(2000,∣V∣) ; undirected graph
Eigenvector Centrality
Computed per connected component; initialization: uniform; maximum iterations: 1000; convergence tolerance: 10−6 ; undirected graph
Closeness Centrality
Computed per connected component; undirected graph
Infomap
Directed graph; unweighted edges
Leiden
Undirected graph; resolution parameter: default
Appendix
Table 3: Graph signal generation parameters used across all evaluated signal categories. All graph-derived signals are computed once on the fixed transaction graph and reused across all evaluation runs to ensure paired comparability.
Model
Parameter
Search Space
GCN
Reduce function
{sum,max,mean}
Message function
SAFE_MSG_FUNCS (DGL message functions)
Activation
{relu,elu,gelu,leaky_relu,silu}
Bias
{True,False}
#Layers
{1,…,4}
Hidden sizes
layer2: 8–127; layer3: 8–63; layer4: 8–31
Appendix
Table 4: Hyperparameter search space for GNN-based models. The table reports the ranges and options considered during automated Bayesian hyperparameter optimization for GCN, GAT, and GCL-based models. All configurations are evaluated under identical optimization budgets and selection criteria.
Figure 12: Maximum F1 -score achieved per graph signal category across all evaluated classifiers. The dashed horizontal line denotes the best-performing transaction-only (TRX) baseline.