  Graph embedding (GE) methods embed nodes (and/or edges) in graph into a
low-dimensional semantic space, and have shown its effectiveness in modeling
multi-relational data. However, existing GE models are not practical in
real-world applications since it overlooked the streaming nature of incoming
data. To address this issue, we study the problem of continual graph
representation learning which aims to continually train a GE model on new data
to learn incessantly emerging multi-relational data while avoiding
catastrophically forgetting old learned knowledge. Moreover, we propose a
disentangle-based continual graph representation learning (DiCGRL) framework
inspired by the human's ability to learn procedural knowledge. The experimental
results show that DiCGRL could effectively alleviate the catastrophic
forgetting problem and outperform state-of-the-art continual learning models.
