Title: GILA: Graph Structure Learning for Class-Imbalanced Tabular Data
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Structure Learning, Graph Neural Networks, Imbalanced data, Tabular data

Abstract: Graph-based learning has demonstrated strong performance across domains by capturing inter-sample dependencies, often surpassing traditional methods focused only on feature-level patterns. However, in tabular data, the relationships between instances are typically implicit or undefined, making it difficult to directly apply graph-based methods. Graph Structure Learning (GSL) addresses this by constructing task-relevant graph structures that explicitly capture the relationships from the data. Nonetheless, a critical yet underexplored challenge in tabular GSL is class imbalance, which is commonly encountered in real-world applications. In this paper, we propose $\textbf{G}$raph Structure Learning for Class-$\textbf{I}$mba$\textbf{l}$anced Dat$\textbf{a}$ ($\textbf{GILA}$) to overcome the difficulties of small-scale and imbalanced tabular datasets.  GILA introduces $\textit{helper nodes}$ to assist the minority class. These helper nodes are optimized using an $\textit{updater}$, and promote class separation among their connected neighbors by influencing their representations through message propagation. Our experiments on 44 imbalanced tabular datasets demonstrate that GILA outperforms existing tabular GSL models. Even under severe class overlap, it still achieves strong performance by leveraging helper nodes that facilitate class discrimination. Furthermore, visualization of the embedding space highlights how helper nodes guide the separation of minority-class samples from majority ones. Code for this submission is provided in an anonymous repository at: https://github.com/anon-gila/GILA

TL;DR: We propose GILA, a graph structure learning framework that introduces helper nodes to enhance the separability of node class representations, leading to improved performance on imbalanced tabular datasets.