  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.
