no code implementations • 20 Feb 2024 • Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-Kiong Ng
Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants.
no code implementations • 10 Oct 2023 • Jinyu Cai, Yunhe Zhang, Jicong Fan
Under the framework, we provide three algorithms with different computational efficiencies and stabilities for anomalous graph detection.
1 code implementation • 13 Feb 2023 • Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan
Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces.
1 code implementation • CVPR 2022 • Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang
The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios.