1 code implementation • 10 Mar 2024 • Liyue Chen, Jiangyi Fang, Tengfei Liu, Shaosheng Cao, Leye Wang
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing.
no code implementations • 6 Feb 2024 • Ruofan Wu, Guanhua Fang, Qiying Pan, Mingyang Zhang, Tengfei Liu, Weiqiang Wang, Wenbiao Zhao
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations.
no code implementations • 31 Oct 2023 • Ruofan Wu, Mingyang Zhang, Lingjuan Lyu, Xiaolong Xu, Xiuquan Hao, Xinyi Fu, Tengfei Liu, Tianyi Zhang, Weiqiang Wang
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).
no code implementations • 17 Oct 2023 • Jiawang Dan, Ruofan Wu, Yunpeng Liu, Baokun Wang, Changhua Meng, Tengfei Liu, Tianyi Zhang, Ningtao Wang, Xing Fu, Qi Li, Weiqiang Wang
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
no code implementations • 18 Sep 2023 • Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, Weiqiang Wang
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.
1 code implementation • 4 Aug 2023 • Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
no code implementations • 30 Nov 2022 • Si Wu, Tengfei Liu, Magnus Egerstedt, Zhong-Ping Jiang
Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues.
1 code implementation • 21 May 2016 • Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K. M. Poon, Zhourong Chen, Farhan Khawar
The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below.
no code implementations • 4 Feb 2014 • Raphaël Mourad, Christine Sinoquet, Nevin L. Zhang, Tengfei Liu, Philippe Leray
In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences.