When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)
Abstract
Cross-domain Graph Meta-learning (CGML) has shown its promise, where meta-knowledge is extracted from few-shot graph data in multiple relevant but distinct domains. However, several recent efforts assume target data available, which commonly does not established in practice. In this paper, we devise a novel Cross-domain Data Augmentation for Graph Meta-Learning (CDA-GML), which incorporates the superiorities of CGML and Data Augmentation， has addressed intractable shortcomings of label sparsity, domain shift, and the absence of target data simultaneously. Specifically, our method simulates instance-level and task-level domain shift to alleviate the cross-domain generalization issue in conventional graph meta-learning. Experiments show that our method outperforms the existing state-of-the-art methods.
Cite
Text
Niu et al. "When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30489Markdown
[Niu et al. "When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/niu2024aaai-sparse/) doi:10.1609/AAAI.V38I21.30489BibTeX
@inproceedings{niu2024aaai-sparse,
title = {{When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)}},
author = {Niu, Simin and Liang, Xun and Zhang, Sensen and Song, Shichao and Zhang, Xuan and Zhou, Xiaoping},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23600-23601},
doi = {10.1609/AAAI.V38I21.30489},
url = {https://mlanthology.org/aaai/2024/niu2024aaai-sparse/}
}