AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching

TL;DR

This paper introduces an AoI-based temporal attention graph neural network that predicts content popularity in dynamic bipartite graphs to improve caching strategies in network systems, enhancing hit rates and prediction accuracy.

Contribution

The paper proposes a novel AoI-based attention mechanism integrated with a dynamic graph neural network for more accurate popularity prediction and caching decisions.

Findings

Higher prediction accuracy than state-of-the-art models

Improved cache hit rate in real-world datasets

Effective handling of message staleness in dynamic graphs

Abstract

Along with the fast development of network technology and the rapid growth of network equipment, the data throughput is sharply increasing. To handle the problem of backhaul bottleneck in cellular network and satisfy people's requirements about latency, the network architecture like information-centric network (ICN) intends to proactively keep limited popular content at the edge of network based on predicted results. Meanwhile, the interactions between the content (e.g., deep neural network models, Wikipedia-alike knowledge base) and users could be regarded as a dynamic bipartite graph. In this paper, to maximize the cache hit rate, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. Furthermore, in order to have deeper insights into the dynamics within the evolving graph, we propose an age of information (AoI) based attention mechanism to extract valuable historical information while avoiding the problem of message staleness. Combining this aforementioned prediction model, we also develop a cache selection algorithm to make caching decisions in accordance with the prediction results. Extensive results demonstrate that our model can obtain a higher prediction accuracy than other state-of-the-art schemes in two real-world datasets. The results of hit rate further verify the superiority of the caching policy based on our proposed model over other traditional ways.