1 code implementation • 6 May 2023 • Hanchi Ren, Jingjing Deng, Xianghua Xie, Xiaoke Ma, Jianfeng Ma
Our proposed learning method is resistant to gradient leakage attacks, and the key-lock module is designed and trained to ensure that, without the private information of the key-lock module: a) reconstructing private training data from the shared gradient is infeasible; and b) the global model's inference performance is significantly compromised.
1 code implementation • 31 Dec 2022 • Xingping Xian, Tao Wu, Xiaoke Ma, Shaojie Qiao, Yabin Shao, Chao Wang, Lin Yuan, Yu Wu
Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated.
no code implementations • 8 Jan 2022 • Ling-Hao Chen, He Li, Wanyuan Zhang, Jianbin Huang, Xiaoke Ma, Jiangtao Cui, Ning li, Jaesoo Yoo
It remains a challenging task to jointly consider all different kinds of interactions and detect anomalous instances on multi-view attributed networks.
2 code implementations • 14 Jul 2020 • Hanchi Ren, Jingjing Deng, Xianghua Xie, Xiaoke Ma, Yichuan Wang
Typical machine learning approaches require centralized data for model training, which may not be possible where restrictions on data sharing are in place due to, for instance, privacy and gradient protection.