Wang Junfang, Guo Jin-Li
Business school, University of Shanghai Science & Technology, Shanghai, China.
School of Mathematics & Statistics, North China University of water Resources & Electric Power, Zhengzhou, China.
PLoS One. 2022 Feb 11;17(2):e0263939. doi: 10.1371/journal.pone.0263939. eCollection 2022.
Reconstruction of network is to infer the relationship among nodes using observation data, which is helpful to reveal properties and functions of complex systems. In view of the low reconstruction accuracy based on small data and the subjectivity of threshold to infer adjacency matrix, the paper proposes two models: the quadratic compressive sensing (QCS) and integer compressive sensing (ICS). Then a combined method (CCS) is given based on QCS and ICS, which can be used on binary-state and multi-state dynamics. It is found that CCS is usually a superior method comparing with compressive sensing, LASSO on several networks with different structures and scales. And it can infer larger node correctly than the other two methods. The paper is conducive to reveal the hidden relationship with small data so that to understand, predicate and control a vast intricate system.
网络重构是利用观测数据推断节点之间的关系，这有助于揭示复杂系统的性质和功能。鉴于基于小数据的重构精度较低以及推断邻接矩阵时阈值的主观性，本文提出了两种模型：二次压缩感知（QCS）和整数压缩感知（ICS）。然后给出了一种基于QCS和ICS的组合方法（CCS），该方法可用于二值状态和多状态动力学。研究发现，在几个具有不同结构和规模的网络上，与压缩感知、LASSO相比，CCS通常是一种更优的方法。并且它比其他两种方法能更正确地推断出更大的节点。本文有助于利用小数据揭示隐藏关系，从而理解、预测和控制庞大复杂的系统。
