earticle

영어

This study develops a systematic, verifiable experimental study to clarify when and how transforming tabular data into graph structures enhances node-level classification: AI-generated synthetic dataset with controlled numbers of nodes, classes, and imbalance; two interpretable graphs are constructed (1) a similarity-based k-nearest neighbors graph with the number of neighbors and model depth varied, and (2) a rule-based graph with explicit, transparent connection rules; and graph-based methods GCN, GraphSAGE to approach this baseline when k and depth are appropriately tuned, before performance saturates or declines due to excessive signal averaging; and rule-based graphs expose architectural differences GraphSAGE is higherperforming and more stable, whereas GCN is more structuresensitive and degrades with depth implying that approaches preserving node-specific information and flexibly aggregating signals are more robust to structural heterogeneity. Overall, the framework offers practical guidance for method selection and graph construction particularly the choice neighbors and depth in a simplified, reproducible form readily extensible to real-world applications.