1 code implementation • 14 Sep 2023 • Jiaren Xiao, Quanyu Dai, Xiao Shen, Xiaochen Xie, Jing Dai, James Lam, Ka-Wai Kwok
To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.
2 code implementations • 31 Aug 2023 • Qijiong Liu, Lu Fan, Jiaren Xiao, Jieming Zhu, Xiao-Ming Wu
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
1 code implementation • IEEE Transactions on Network Science and Engineering 2023 • Jiaren Xiao, Quanyu Dai, Xiaochen Xie, Qi Dou, Ka-Wai Kwok, James Lam
The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks.
1 code implementation • 7 Jun 2021 • Jiaren Xiao, Quanyu Dai, Xiaochen Xie, James Lam, Ka-Wai Kwok
The high cost of data labeling often results in node label shortage in real applications.
1 code implementation • 4 Sep 2019 • Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, Dan Wang
Existing methods for single network learning cannot solve this problem due to the domain shift across networks.