让图神经网络自动构建更相关的数据图，提升表格与关系数据预测效果。
From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs
- 根据任务重要性动态把字段转为节点，增强图结构。
- 在多个真实数据集上显著优于传统键值连接的图构建方法。
- 适合处理表格和关系型数据的模型改进，尤其关注预测性能。
表格和关系型数据是现实世界机器学习中最为普遍的数据格式，涵盖金融、医疗等多个领域。尽管这类数据具有结构化表示，但对现代深度学习方法（通常假设输入为扁平对齐特征）构成挑战。图神经网络（GNNs）通过捕捉表内及表间结构依赖，成为有前景的解决方案。然而，现有基于GNN的方法多依赖于固定的、基于模式的图结构（如主键-外键链接），未能充分利用非键属性中的丰富预测信号。本文提出auGraph，一种统一的任务感知图增强框架，适用于表格与关系型数据。auGraph通过评分函数量化属性对下游预测任务的相关性，选择性地将属性提升为节点，以增强基础图结构，同时保留原始数据模式。该方法在不改变原始数据结构的前提下注入任务相关结构信号。实验表明，auGraph在多个真实数据集上显著优于基于模式或启发式规则的图构建方法，生成的图能更好支持关系与表格预测任务的学习。
原文摘要 · Abstract (English)
Tabular and relational data remain the most ubiquitous formats in real-world machine learning applications, spanning domains from finance to healthcare. Although both formats offer structured representations, they pose distinct challenges for modern deep learning methods, which typically assume flat, feature-aligned inputs. Graph Neural Networks (GNNs) have emerged as a promising solution by capturing structural dependencies within and between tables. However, existing GNN-based approaches often rely on rigid, schema-derived graphs -- such as those based on primary-foreign key links -- thereby underutilizing rich, predictive signals in non key attributes. In this work, we introduce auGraph, a unified framework for task-aware graph augmentation that applies to both tabular and relational data. auGraph enhances base graph structures by selectively promoting attributes into nodes, guided by scoring functions that quantify their relevance to the downstream prediction task. This augmentation preserves the original data schema while injecting task-relevant structural signal. Empirically, auGraph outperforms schema-based and heuristic graph construction methods by producing graphs that better support learning for relational and tabular prediction tasks.
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