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

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