A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning

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.

ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities

Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific properties of the nodes. A key challenge in the field is developing scalable methods that can preserve structural properties suitable for the required types of structural patterns of a given downstream application task. While most existing methods focus on preserving node proximity, those that do preserve structural properties often lack the flexibility to preserve various types of structural patterns required by downstream application tasks. This paper introduces ffstruc2vec, a scalable deep-learning framework for learning node embedding vectors that preserve structural identities. Its flat, efficient architecture allows high flexibility in capturing diverse types of structural patterns, enabling broad adaptability to various downstream application tasks. The proposed framework significantly outperforms existing approaches across diverse unsupervised and supervised tasks in practical applications. Moreover, ffstruc2vec enables explainability by quantifying how individual structural patterns influence task outcomes, providing actionable interpretation. To our knowledge, no existing framework combines this level of flexibility, scalability, and structural interpretability, underscoring its unique capabilities.