  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.
