@inproceedings{ebcf2a1d57154824b36f14bae5eafaa4,
title = "Elastic Graph Neural Networks",
abstract = "While many existing graph neural networks (GNNs) have been proven to perform `2-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via `1-based graph smoothing. As a result, we introduce a family of GNNs (Elastic GNNs) based on `1 and `2-based graph smoothing. In particular, we propose a novel and general message passing scheme into GNNs. This message passing algorithm is not only friendly to back-propagation training but also achieves the desired smoothing properties with a theoretical convergence guarantee. Experiments on semi-supervised learning tasks demonstrate that the proposed Elastic GNNs obtain better adaptivity on benchmark datasets and are significantly robust to graph adversarial attacks. The implementation of Elastic GNNs is available at https://github.com/lxiaorui/ElasticGNN.",
author = "Xiaorui Liu and Wei Jin and Yao Ma and Yaxin Li and Hua Liu and Yiqi Wang and Ming Yan and Jiliang Tang",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 by the author(s); 38th International Conference on Machine Learning, ICML 2021 ; Conference date: 18-07-2021 Through 24-07-2021",
year = "2021",
language = "English (US)",
series = "Proceedings of Machine Learning Research",
publisher = "ML Research Press",
pages = "6837--6849",
booktitle = "Proceedings of the 38th International Conference on Machine Learning, ICML 2021",
}