Data Imputation with Iterative Graph Reconstruction

Authors

  • Jiajun Zhong Central South University
  • Ning Gui Central South University
  • Weiwei Ye Central South University

DOI:

https://doi.org/10.1609/aaai.v37i9.26348

Keywords:

ML: Graph-based Machine Learning, ML: Relational Learning, ML: Representation Learning, ML: Unsupervised & Self-Supervised Learning

Abstract

Effective data imputation demands rich latent ``structure" discovery capabilities from ``plain" tabular data. Recent advances in graph neural networks-based data imputation solutions show their structure learning potentials by translating tabular data as bipartite graphs. However, due to a lack of relations between samples, they treat all samples equally which is against one important observation: ``similar sample should give more information about missing values." This paper presents a novel Iterative graph Generation and Reconstruction framework for Missing data imputation(IGRM). Instead of treating all samples equally, we introduce the concept: ``friend networks" to represent different relations among samples. To generate an accurate friend network with missing data, an end-to-end friend network reconstruction solution is designed to allow for continuous friend network optimization during imputation learning. The representation of the optimized friend network, in turn, is used to further optimize the data imputation process with differentiated message passing. Experiment results on eight benchmark datasets show that IGRM yields 39.13% lower mean absolute error compared with nine baselines and 9.04% lower than the second-best. Our code is available at https://github.com/G-AILab/IGRM.

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Published

2023-06-26

How to Cite

Zhong, J., Gui, N., & Ye, W. (2023). Data Imputation with Iterative Graph Reconstruction. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 11399-11407. https://doi.org/10.1609/aaai.v37i9.26348

Issue

Section

AAAI Technical Track on Machine Learning IV