AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised
  Contrastive Learning
Xiaochuan Zhang Mengran Li Ye Wang Haojun Fei
Attribute graphs are ubiquitous in multimedia applications, and graph
representation learning (GRL) has been successful in analyzing attribute graph
data. However, incomplete graph data and missing node attributes can have a
negative impact on media knowledge discovery. Existing methods for handling
attribute missing graph have limited assumptions or fail to capture complex
attribute-graph dependencies. To address these challenges, we propose Attribute
missing Graph Contrastive Learning (AmGCL), a framework for handling missing
node attributes in attribute graph data. AmGCL leverages Dirichlet energy
minimization-based feature precoding to encode in missing attributes and a
self-supervised Graph Augmentation Contrastive Learning Structure (GACLS) to
learn latent variables from the encoded-in data. Specifically, AmGCL utilizies
feature reconstruction based on structure-attribute energy minimization while
maximizes the lower bound of evidence for latent representation mutual
information. Our experimental results on multiple real-world datasets
demonstrate that AmGCL outperforms state-of-the-art methods in both feature
imputation and node classification tasks, indicating the effectiveness of our
proposed method in real-world attribute graph analysis tasks.