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Dynamic-GTN: Learning an Node Efficient Embedding in Dynamic Graph with Transformer

Hoang, Thi Linh and Ta, Viet Cuong Dynamic-GTN: Learning an Node Efficient Embedding in Dynamic Graph with Transformer. In: 19th Pacific Rim International Conference on Artificial Intelligence. (In Press)

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Graph Transformer Networks (GTN) use attention mechanism to learn the node representation in a static graph and can achieve state-of-the-art results on several graph learning tasks. However, due to the computation complexity of the attention operation, these models are not applicable to dynamic graphs. In this paper, we propose the Dynamic-GTN model which is designed to learn the node embedding in a continous-time dynamic graph. The Dynamic-GTN extends the attention mechanism in a standard GTN to include temporal information of recent node interaction. Based on temporal patterns interaction between nodes, an node sampling step is added to reduce the number of attention connections in the dynamic graph. We evaluate our model on three benchmark datasets for learning node embedding in dynamic graphs. The results show that the Dynamic-GTN has better accuracy than the state-of-the-art of graph neural networks on both transductive and inductive graph learning tasks.

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
ISI/Scopus indexed conference
Divisions: Faculty of Information Technology (FIT)
Depositing User: Thi Linh Hoang
Date Deposited: 22 Aug 2022 04:06
Last Modified: 22 Aug 2022 04:06

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