通过对抗加权链路预测迈向网络动态的统一方法
Towards a Unified Method for Network Dynamic via Adversarial Weighted Link Prediction
AI总结：
本文提出统一的基于预测的方法处理网络动态，将动态预测表述为时序链路预测任务，并开发结合GAN的HQ-TLP模型生成高质量预测快照，支持精确网络控制，实验证明其强大能力。
摘要：
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（例如，吞吐量、容量、延迟和抖动）有很大影响。本文提出了一种统一的基于预测的方法来处理各种网络系统的动态。从图深度学习的角度，我通常将网络的动态预测表述为时序链路预测任务，并分析加权网络预测可能面临的挑战，其中链路权重存在宽值范围和稀疏性问题。受生成对抗网络（GAN）进行高分辨率视频帧预测的启发，我尝试采用对抗学习为网络动态生成高质量的预测快照，期望支持精确且细粒度的网络控制。随后，开发了一种结合GAN的新型高质量时序链路预测（HQ-TLP）模型，以说明我基本想法的潜力。针对各种应用场景的大量实验进一步证明了HQ-TLP的强大能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
