Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupled with spectral concentration, which means the spectrum obtained by GNNs concentrates on a local part in spectral domain, e.g., low-frequency due to oversmoothing issue. As a consequence, GNNs may be seriously flawed for reconstructing graph attributes as graph spectral concentration tends to cause a low imputation precision. In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy. Notably, we first present the method for estimating graph spectral entropy without the eigen-decomposition of Laplacian matrix and provide the theoretical upper error bound. A maximum entropy regularization then acts in the latent space, which directly increases the graph spectral entropy. Extensive experiments show that MEGAE outperforms all the other state-of-the-art imputation methods on a variety of benchmark datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeImputationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data
Imputation and propensity score weighting are two popular techniques for handling missing data. We address these problems using the regularized M-estimation techniques in the reproducing kernel Hilbert space. Specificall…
ImputationregressionHigh-dimensional Time Series Prediction with Missing Values
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology. Often, such applications require methods that are both highly scalable, and deal with noisy data in term…
Demand ForecastingMatrix CompletionMissing ValuesPrediction+4Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains
Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers. However, the development of effective automated se…
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand need…
Demand ForecastingMissing ValuesTime SeriesTime Series Analysis+2Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
Our study concerns with automated predicting of congestive heart failure (CHF) through the analysis of electrocardiography (ECG) signals. A novel machine learning approach, regularized hessenberg decomposition based extr…
BIG-bench Machine LearningElectrocardiography (ECG)