研究成果：Xiao Han, Zhesi Shen, Wen-Xu Wang, and Zengru Di. Robust Reconstruction of Complex Networks from Sparse Data. Phys. Rev. Lett. 114, 028701 – Published 14 January 2015

简介：

       从获得的数据重构出系统的网络结构是一个重要的问题，是控制和预测系统的基础。当获取的数据量很少，并且有噪声或者缺失部分节点信息的时候，更加加大了重构网络结构的难度。2015年1月14日，我院王文旭教授及其科研团队在PRL上发表了基于the lasso方法鲁棒性重构复杂网络的文章，并用最后通牒博弈，电阻网络，通信网络很好的验证了该方法。

        Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a significant challenge arises when we attempt to decode structural information hidden in limited amounts of data accompanied by noise and in the presence of inaccessible nodes. Here, we develop a general framework for robust reconstruction of complex networks from sparse and noisy data. Specifically, we decompose the task of reconstructing the whole network into recovering local structures centered at each node. Thus, the natural sparsity of complex networks ensures a conversion from the local structure reconstruction into a sparse signal reconstruction problem that can be addressed by using the lasso, a convex optimization method. We apply our method to evolutionary games, transportation, and communication processes taking place in a variety of model and real complex networks, finding that universal high reconstruction accuracy can be achieved from sparse data in spite of noise in time series and missing data of partial nodes. Our approach opens new routes to the network reconstruction problem and has potential applications in a wide range of fields.

原文链接：

http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.114.028701

（系统科学学院）