Topology Prediction and Structural Controllability Analysis of Complex Networks Without Connection Information
针对缺乏全局连接信息的复杂网络，提出基于连接概率矩阵的拓扑预测方法，以获取全局结构并分析其结构可控性，通过人工和真实网络仿真验证了方法的准确性。
In this article, we consider complex networks without connection information. The absence of global structure induces a great obstacle in the structural controllability analysis of these networks. Thus, a topology predicting method based on the connection probability matrix is proposed to provide a global structure for structural controllability analysis in this article. Furthermore, the modified principles of predicting global network topologies are established to acquire a more accurate global connection relationship. Eventually, the drive node set of these networks is determined by predicting global topologies. The accuracy of the proposed topology predicting method is verified by numerical simulations in the context of artificial networks and real networks. The results reveal that the global topology and structural controllability of complex networks with large scale and high edge density could be accurately predicted by utilizing the proposed method.