研究成果：Han, X., Shen, Z., Wang, W. X., Lai, Y. C., & Grebogi, C. (2016). Reconstructing direct and indirect interactions in networked public goods game. Scientific Reports, 6.
复杂网络重构是理解复杂系统关系的基础。当网络中存在间接相互作用和直接相互作用时，如何通过重构去区分两种相互作用，并挖掘出原始的网络是一个问题。网络上的公共品博弈中同时存在直接相互作用和间接相互作用。为了区分网络公共品博弈中的直接相互作用和间接相互作用，我们首先用the lasso方法重构出包括直接相互作用和间接相互作用的网络，然后用matrix transformation和linear least squares method可以分开直接相互作用和间接相互作用。同时，这个方法也能够让我们定位网络中的隐藏节点。我们进行了一系列的模拟，模拟结果显示了很好的重构效果。
Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.