Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie Zhou, Yan Zhang · 2020
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 graph embedding 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 (DiC-GRL) 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.* This work is done when Xiaoyu Kou was interning at Pattern Recognition Center, WeChat AI, Tencent Inc, China !"#"$% &'"(" )*$+,--, &'"(" )"-*" .//&'"(" .//,01/+"( 2#,3*4,/5 6+, 7/*5,4 85"5,3 85"5, 9: ;",# ?#"3,# @.B9'*/39/ ?*#35 ="4> 9: 78 @+*$"C9