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

arXiv 2411.04907 ↗First posted 7 Nov 2024cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 7 Nov 2024.

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21 of 43 references.

References · 21

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 680
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  6. 2020Handling Missing Data with Graph Representation Learningcited by 133
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  8. 2020Missing Data Imputation using Optimal Transportcited by 100
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  10. 2022Theory of Graph Neural Networks: Representation and Learningcited by 61
  11. 2019Missing Data Imputation with Adversarially-trained Graph Convolutional Networkscited by 54
  12. 2021MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanismscited by 51
9 more
  1. 2018Processing of missing data by neural networkscited by 37
  2. 2023The Expressive Power of Graph Neural Networks: A Surveycited by 27
  3. 2017MIDA: Multiple Imputation using Denoising Autoencoderscited by 20
  4. 2021Edge-Featured Graph Attention Networkcited by 18
  5. 2021Deconvolutional Networks on Graph Datacited by 16
  6. 2022Data Imputation with Iterative Graph Reconstructioncited by 15
  7. 2023Confidence-Based Feature Imputation for Graphs with Partially Known Featurescited by 14
  8. 2022EGG-GAE: scalable graph neural networks for tabular data imputationcited by 7
  9. 2023GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Setscited by 7

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