Temporal graphs serve as a powerful framework for representing networks whose connections evolve over time. By incorporating time‐stamped interactions, these models capture the dynamic nature of ...

Abstract: Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatiotemporal forecasting problems. While many dynamic graph construction methods have been ...

Graph mining and subgraph analysis encompass a suite of techniques dedicated to unearthing hidden patterns, recurring structures and relationships within complex networks. This field has rapidly ...

Abstract: Graph convolutional networks (GCNs) are the most commonly used methods recently and have greatly pro-moted research on skeleton-based action recognition. Aggregating features guided by a ...

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 forgetting problem, where ...