121 citations · 324 across the 17 of their papers we have counts for
5 papers · 1 filter
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang +4
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-…
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4
Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Juyong Jiang, Binqing Wu, Ling Chen +2
Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segment…