Masked Graph

Masked graph learning is a self-supervised approach to graph representation learning that involves masking (removing) parts of a graph's structure (nodes or edges) and then training a model to reconstruct the missing information. Current research focuses on improving the robustness and explainability of these models, addressing challenges like out-of-distribution generalization and developing effective masking strategies and architectures, including graph autoencoders, graph transformers, and contrastive learning methods. This technique is proving valuable for various applications, such as multimodal emotion recognition, architectural layout generation, and anomaly detection in complex systems like those used to detect advanced persistent threats, by enabling efficient and effective learning from unlabeled graph data.