TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks
Yufeng Wang, Charith Mendis · 2024
In recent years, Temporal Graph Neural Networks (TGNNs) have achieved great success in learning tasks for graphs that change over time. These dynamic/temporal graphs represent topology changes as either discrete static graph snapshots (called DTDGs), or a continuous stream of timestamped edges (called CTDGs). Because continuous-time graphs have richer time information, it will be crucial to have abstractions for programming CTDG-based models so that practitioners can easily explore new designs and optimizations in this space. A few recent frameworks have been proposed for programming and accelerating TGNN models, but these either do not support continuous-time graphs, lack easy composability, and/or do not facilitate CTDG-specific optimizations.