The enormous amount of network equipment and users implies a tremendous growth of Internet traffic for multimedia services. To mitigate the traffic pressure, architectures with in-network storage are proposed to cache popular content at nodes in close proximity to users to shorten the backhaul links. Meanwhile, the reduction of transmission distance also contributes to the energy saving. However, due to limited storage, only a fraction of the content can be cached, while caching the most popular content is cost-effective. Correspondingly, it becomes essential to devise an effective popularity prediction method. In this regard, existing efforts adopt dynamic graph neural network (DGNN) models, but it remains challenging to tackle sparse datasets. In this paper, we first propose a reformative temporal graph network, which is named STGN, that utilizes extra semantic messages to enhance the temporal and structural learning of a DGNN model, since the consideration of semantics can help establish implicit paths within the sparse interaction graph and hence improve the prediction performance. Furthermore, we propose a user-specific attention mechanism to fine-grainedly aggregate various semantics. Finally, extensive simulations verify the superiority of our STGN models and demonstrate their high potential in energy-saving.

翻译：网络设备与用户数量的激增导致多媒体服务互联网流量急剧增长。为缓解流量压力，具备网络内存储能力的架构被提出，通过在靠近用户的节点缓存流行内容来缩短回程链路。同时，传输距离的缩减也有助于节能。然而，受限于存储空间，仅能缓存部分内容，而缓存最流行内容最具成本效益。因此，设计有效的流行度预测方法至关重要。现有研究采用动态图神经网络（DGNN）模型，但处理稀疏数据集仍具挑战性。本文首先提出一种改进的时间图网络STGN，通过利用额外语义信息增强DGNN模型的时间与结构学习能力——语义考量有助于在稀疏交互图中建立隐式路径，从而提升预测性能。进一步，我们设计用户专属注意力机制以细粒度聚合多种语义。最后，大量仿真验证了STGN模型的优越性，并展示其在节能方面的巨大潜力。