Attribute imputation autoencoders for attribute-missing graphs

Abstract

Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data imputation techniques based on graph autoencoders are commonly used for attribute-missing graphs. However, this method cannot effectively integrate existing attributes and structural information during the encoding stage and is prone to introducing noise, resulting in inaccurate imputation. In addition, the expressiveness of decoders in existing methods is limited because their network architecture has not been adequately designed, which restricts the accuracy and robustness of the generated attributes. To address these issues, we propose a novel Attribute Imputation AutoEncoder for attribute-missing graphs, named AIAE. In particular, during the encoding stage, a dual encoder based on knowledge distillation is designed to encode both attribute and structural information into representations of attribute-missing nodes to achieve more accurate imputation. To avoid introducing noise, we fully exploit the observed information by reorganizing the representations of the attribute-missing and attribute-observed nodes. In the decoding stage, we propose a multi-scale decoder with masking to make the decoder more expressive and enhance its robustness and generative ability. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art methods in attribute-missing graphs.