Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN) Background Introduction A temporal graph is a graph structure with dynamic interactions between nodes over continuous time, where the topology evolves over time. Such dynamic changes enable nodes to exhibit varying preferences at different times, which is critical for capturing us...

Knowledge Enhanced Graph Neural Networks for Explainable Recommendation Introduction With the explosive growth of online information, recommendation systems play an essential role in solving the problem of information overload. Traditional recommendation systems typically rely on Collaborative Filtering (CF) methods, which generate recommendations ...

A Framework for Solving Graph Optimization Problems Based on Graph Neural Networks Background and Research Motivation In solving Constraint Satisfaction Problems (CSPs) and Combinatorial Optimization Problems (COPs), a common method is the combination of backtracking and branch heuristics. Although branch heuristics designed for specific problems a...

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...

Adapting to Open-World Events via Learning Spatio-Temporal Dynamics on Mobility Networks Research Background In modern society, the Mobility-as-a-Service (MaaS) system is seamlessly integrated by various transportation modes (such as public transport, ride-sharing, and shared bicycles). To achieve efficient MaaS operation, modeling the spatio-tempo...