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.

翻译：随着网络技术的快速发展和网络设备迅速增长,数据传输量正在急剧增加。为了处理蜂窝网络中的回光壳瓶颈问题,满足人们对潜伏性的要求,信息中心网络(ICN)等网络结构打算根据预测结果,在网络边缘积极保持有限的受欢迎内容。与此同时,内容(如深神经网络模型、类似维基百科的知识库)和用户之间的相互作用可被视为动态双方图。在本文中,为了最大限度地增加缓冲率,我们利用一个有效的动态图形神经网络(DGNN)来联合学习双部分图中嵌入的结构和时间模式。此外,为了更深入了解演变图中的变化动态,我们提议了一个信息时代(AoI)的注意机制,以提取宝贵的历史信息,同时避免电流问题。结合了上述预测模型,我们还开发了一个缓存选择算法,以便根据预测结果作出准确的决定。广泛的结果表明,我们的模型可以获取比其他数据水平更高的真实的预测率。