5 papers
Incident-Guided Spatiotemporal Traffic Forecasting
Lixiang Fan, Bohao Li, Tao Zou +2
Recent years have witnessed the rapid development of deep-learning-based, graph-neural-network-based forecasting methods for modern intelligent transportation systems. However, mos…
Global-Lens Transformers: Adaptive Token Mixing for Dynamic Link Prediction
Tao Zou, Chengfeng Wu, Tianxi Liao +2
Dynamic graph learning plays a pivotal role in modeling evolving relationships over time, especially for temporal link prediction tasks in domains such as traffic systems, social n…
Repeat-Aware Neighbor Sampling for Dynamic Graph Learning
Tao Zou, Yuhao Mao, Junchen Ye +1
Dynamic graph learning equips the edges with time attributes and allows multiple links between two nodes, which is a crucial technology for understanding evolving data scenarios li…
Pretraining Language Models with Text-Attributed Heterogeneous Graphs
Tao Zou, Le Yu, Yifei Huang +2
In many real-world scenarios (e.g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships…
Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction
Tao Zou, Le Yu, Leilei Sun +3
Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential p…