relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4776/
title: Dynamic-GTN: Learning an Node Efficient Embedding in Dynamic Graph with Transformer
creator: Hoang, Thi Linh
creator: Ta, Viet Cuong
subject: Information Technology (IT)
subject: ISI/Scopus indexed conference
description: 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.
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4776/1/PRICAI_2022_paper_119%20%281%29.pdf
identifier:   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)