IEEE Trans Cybern. 2022 Jun;52(6):5136-5147. doi: 10.1109/TCYB.2020.3027642. Epub 2022 Jun 16.
The problem of reconstructing nonlinear and complex dynamical systems from available data or time series is prominent in many fields, including engineering, physical, computer, biological, and social sciences. Many methods have been proposed to address this problem and their performance is satisfactory. However, none of them can reconstruct network structure from large-scale real-time streaming data, which leads to the failure of real-time and online analysis or control of complex systems. In this article, to overcome the limitations of current methods, we first extend the network reconstruction problem (NRP) to online settings, and then develop a follow-the-regularized-leader (FTRL)-Proximal style method to address the online complex NRP; we refer to it as Online-NR. The performance of Online-NR is validated on synthetic evolutionary game network reconstruction datasets and eight real-world networks. The experimental results demonstrate that Online-NR can effectively solve the problem of online network reconstruction with large-scale real-time streaming data. Moreover, Online-NR outperforms or matches nine state-of-the-art network reconstruction methods.
从现有数据或时间序列中重建非线性和复杂动力系统的问题在许多领域都很突出，包括工程、物理、计算机、生物和社会科学。已经提出了许多方法来解决这个问题，并且它们的性能令人满意。然而，它们都无法从大规模实时流数据中重建网络结构，这导致了复杂系统的实时和在线分析或控制的失败。在本文中，为了克服当前方法的局限性，我们首先将网络重建问题（NRP）扩展到在线设置，然后开发了一种跟随正则化领导者（FTRL）的近邻风格方法来解决在线复杂 NRP；我们称之为 Online-NR。在合成进化博弈网络重建数据集和八个真实网络上验证了 Online-NR 的性能。实验结果表明，Online-NR 可以有效地解决具有大规模实时流数据的在线网络重建问题。此外，Online-NR 优于或匹配九种最先进的网络重建方法。
