AoI-based Temporal Attention Graph Neural Network for Popularity Prediction in ICN

With the development of network technology and the rapid growth of network equipment, the data throughput in the network is sharply increasing. To meet people’s requirements for low latency, the network architecture like Information-Centric Network (ICN) proposes to keep part of the content at the edge of network. In this paper, to maximize the cache hit rate, we propose a prediction model based on dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph between users and visited content for predicting the content popularity. Furthermore, in order to strengthen the dynamic learning of graphs, we propose an age of information (AoI) based attention mechanism to extract useful historical information while avoiding the problem of message staleness. Extensive simulation results demonstrate that our model can obtain higher prediction accuracy, and generate a caching policy with boosted caching hits.