21 citations · 24 across the 5 of their papers we have counts for
5 papers
A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units
Liyue Chen, Jiangyi Fang, Tengfei Liu +2
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region pa…
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu +6
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, h…
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Qiying Pan, Ruofan Wu, Tengfei Liu +3
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 preservi…
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Jiapu Wang, Boyue Wang, Meikang Qiu +8
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 i…