Initializing Then Refining: A Simple Graph Attribute Imputation Network

Representation learning on the attribute-missing graphs, whose connection information is complete while the attribute information of some nodes is missing, is an important yet challenging task. To impute the missing attributes, existing methods isolate the learning processes of attribute and structure information embeddings, and force both resultant representations to align with a common in-discriminative normal distribution, leading to inaccurate imputation. To tackle these issues, we propose a novel graph-oriented imputation framework called initializing then refining (ITR), where we first employ the structure information for initial imputation, and then leverage observed attribute and structure information to adaptively refine the imputed latent variables. Specifically, we first adopt the structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by aggregating the reliable and informative embeddings of attribute-observed samples according to the affinity structure. Specially, in our refining process, the affinity structure is adaptively updated through iterations by calculating the sample-wise correlations upon the recomposed embeddings. Extensive experiments on four benchmark datasets verify the superiority of ITR against state-of-the-art methods.

Authors

• Wenxuan Tu School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, China
• Sihang Zhou College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
• Xinwang Liu National University of Defense Technology
• Yue Liu Institute of Data Science & School of Computing, National University of Singapore
• Zhiping Cai National University of Defense Technology
• En Zhu National University of Defense Technology
• Changwang Zhang Huawei Poisson Lab
• Jieren Cheng School of Computer Science and Technology, Hainan University, Haikou, China Hainan Blockchain Technology Engineering Research Center, Haikou, China

Keywords

• Machine Learning: Autoencoders
• Machine Learning: Multi-view learning
• Machine Learning: Representation learning
• Machine Learning: Self-supervised Learning