School of Automation, Central South University, Changsha 410083, China; Peng Cheng Laboratory, Shenzhen 518055, China.
School of Automation, Central South University, Changsha 410083, China.
Neural Netw. 2020 Jun;126:143-152. doi: 10.1016/j.neunet.2020.03.008. Epub 2020 Mar 14.
Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice.
复杂网络是一种通用模型，用于表示技术、社会、信息和生物相互作用中的相互作用。通常，直接检测相互作用关系的代价很高。因此，网络结构重建是复杂网络系统中的逆问题，对于理解具有未知相互作用结构的许多复杂系统至关重要。此外，从真实网络系统中收集的数据通常会受到噪声的污染，这使得网络结构推断任务更加具有挑战性。在本文中，我们开发了一种基于深度学习方法的博弈动力学网络结构重建的新框架。与使用计算复杂的凸/贪婪算法来解决网络重建任务的压缩感知方法不同，我们引入了一种深度学习框架，可以从节点数据中学习结构化表示，并在很少的观测数据下有效地重建博弈动力学网络结构。具体来说，我们提出了去噪自动编码器（DAEs）作为无监督特征学习器，以捕获不同节点之间的统计依赖关系。与基于压缩感知的方法相比，所提出的方法是一种全局网络结构推断方法，不仅可以获得最先进的性能，还可以直接获得网络的结构。此外，所提出的方法对观测数据中的噪声具有鲁棒性。此外，所提出的方法对于非精确稀疏的网络也是有效的。因此，所提出的方法可以扩展到实际中广泛的网络重建任务。
