no code implementations • 8 Dec 2023 • Yakun Wang, Binbin Hu, Shuo Yang, Meiqi Zhu, Zhiqiang Zhang, Qiyang Zhang, Jun Zhou, Guo Ye, Huimei He
In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner.
1 code implementation • 28 Jan 2021 • Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks.
no code implementations • 5 Jul 2020 • Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei
We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).
2 code implementations • 5 Feb 2020 • Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui
The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.