Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph

TL;DR

This paper introduces BCGNN, a novel graph neural network framework that models feature interdependencies to significantly improve missing data imputation accuracy in tabular datasets.

Contribution

The paper presents a new GNN framework combining bipartite and complete directed graphs to better capture feature interdependencies for missing data imputation.

Findings

Achieves 15% reduction in mean absolute error compared to existing methods.

Effectively models complex feature interdependencies.

Generalizes well to unseen data points.

Abstract

In this paper, we aim to address a significant challenge in the field of missing data imputation: identifying and leveraging the interdependencies among features to enhance missing data imputation for tabular data. We introduce a novel framework named the Bipartite and Complete Directed Graph Neural Network (BCGNN). Within BCGNN, observations and features are differentiated as two distinct node types, and the values of observed features are converted into attributed edges linking them. The bipartite segment of our framework inductively learns embedding representations for nodes, efficiently utilizing the comprehensive information encapsulated in the attributed edges. In parallel, the complete directed graph segment adeptly outlines and communicates the complex interdependencies among features. When compared to contemporary leading imputation methodologies, BCGNN consistently outperforms them, achieving a noteworthy average reduction of 15% in mean absolute error for feature imputation tasks under different missing mechanisms. Our extensive experimental investigation confirms that an in-depth grasp of the interdependence structure substantially enhances the model's feature embedding ability. We also highlight the model's superior performance in label prediction tasks involving missing data, and its formidable ability to generalize to unseen data points.