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• W3099554319 abstract "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. The code and datasets are released on https://github.com/KXY-PUBLIC/DiCGRL." @default.
• W3099554319 created "2020-11-23" @default.
• W3099554319 creator A5029799433 @default.
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• W3099554319 date "2020-01-01" @default.
• W3099554319 modified "2023-10-14" @default.
• W3099554319 title "Disentangle-based Continual Graph Representation Learning" @default.
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• W3099554319 doi "https://doi.org/10.18653/v1/2020.emnlp-main.237" @default.
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