98 citations · 131 across the 5 of their papers we have counts for
5 papers
Dual-Channel Multiplex Graph Neural Networks for Recommendation
Xiang Li, Chaofan Fu, Zhongying Zhao +4
Effective recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have…
Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Wei Chen, Chao Huang, Yanwei Yu +2
Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns. Existing works mai…
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +4
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to est…
Multiplex Heterogeneous Graph Convolutional Network
Pengyang Yu, Chaofan Fu, Yanwei Yu +3
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to…
Scalable Motif Counting for Large-scale Temporal Graphs
Zhongqiang Gao, Chuanqi Cheng, Yanwei Yu +3
One fundamental problem in temporal graph analysis is to count the occurrences of small connected subgraph patterns (i.e., motifs), which benefits a broad range of real-world appli…