6 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…
Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
Tao Zou, Le Yu, Junchen Ye +3
Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus o…