用并行计算与多方向正则化，高效重建大规模隐含网络。
Parallel Network Reconstruction with Multi-directional Regularization
- 将节点分块并行估计子网络，降低计算与存储开销。
- 理论证明估计器一致性，仿真与真实数据验证效果稳定。
- 适合超大规模网络重建，尤其适用于分布式系统。
从观测动态中重建大规模隐含网络对理解复杂系统至关重要。然而，基于压缩感知的现有方法因计算和内存成本过高而难以实际应用。为此，我们提出一种新的分布式计算框架PALMS（Parallel Adaptive Lasso with Multi-directional Signals），通过节点划分实现全局问题的分解，使多个计算单元可并行估计子网络，显著降低经典方法的计算复杂度与存储需求。在每个计算单元上引入自适应多方向正则化，理论上建立了PALMS估计器的一致性。大量仿真研究及在多个大规模真实网络上的实证分析验证了该方法在计算效率和重建准确性方面的优越性。
原文摘要 · Abstract (English)
Reconstructing large-scale latent networks from observed dynamics is crucial for understanding complex systems. However, the existing methods based on compressive sensing are often rendered infeasible in practice by prohibitive computational and memory costs. To address this challenge, we introduce a new distributed computing framework for efficient large-scale network reconstruction with parallel computing, namely PALMS (Parallel Adaptive Lasso with Multi-directional Signals). The core idea of PALMS is to decompose the complex global problem by partitioning network nodes, enabling the parallel estimation of sub-networks across multiple computing units. This strategy substantially reduces the computational complexity and storage requirements of classic methods. By using the adaptive multi-directional regularization on each computing unit, we also establish the consistency of PALMS estimator theoretically. Extensive simulation studies and empirical analyses on several large-scale real-world networks validate the computational efficiency and robust reconstruction accuracy of our approach.
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