Semantics-enhanced Temporal Graph Networks for Content Popularity Prediction

Understand

The surging demand for high-definition video streaming services and large neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a tremendous explosion of Internet traffic.

• To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users.
• Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method.
• In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieve remarkable performance.