2024/01/07 by Meng Qin, Qin, Meng
Physics and Astronomy · Social Sciences · #Advanced Computing and Algorithms #Complex Network Analysis Techniques #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2401.03444
openalex publication_date 2024/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.