Network dynamic (e.g., traffic burst in data center networks and channel fading in cellular WiFi networks) has a great impact on the performance of communication networks (e.g., throughput, capacity, delay, and jitter). This article proposes a unified prediction-based method to handle the dynamic of various network systems. From the view of graph deep learning, I generally formulate the dynamic prediction of networks as a temporal link prediction task and analyze the possible challenges of the prediction of weighted networks, where link weights have the wide-value-range and sparsity issues. Inspired by the high-resolution video frame prediction with generative adversarial network (GAN), I try to adopt adversarial learning to generate high-quality predicted snapshots for network dynamic, which is expected to support the precise and fine-grained network control. A novel high-quality temporal link prediction (HQ-TLP) model with GAN is then developed to illustrate the potential of my basic idea. Extensive experiments for various application scenarios further demonstrate the powerful capability of HQ-TLP.

翻译：网络动态（例如数据中心网络中的流量突发和蜂窝WiFi网络中的信道衰落）对通信网络的性能（如吞吐量、容量、时延和抖动）有显著影响。本文提出了一种统一的基于预测的方法来处理各类网络系统的动态。从图深度学习的视角出发，我将网络动态预测一般性地表述为时序链路预测任务，并分析了加权网络预测可能面临的挑战，其中链路权重存在值域广泛和稀疏性问题。受生成对抗网络（GAN）高分辨率视频帧预测的启发，我尝试采用对抗性学习来生成网络动态的高质量预测快照，以支持精确且细粒度的网络控制。进而，一种基于GAN的新型高质量时序链路预测（HQ-TLP）模型被开发出来，以展示我基本思想的潜力。针对多种应用场景的广泛实验进一步证明了HQ-TLP的强大能力。