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.

中文翻译：

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SDARE：一种用于游戏动态网络结构重建的堆叠式去噪自动编码器方法。

复杂网络是代表技术，社会，信息和生物相互作用内相互作用的通用模型。通常，直接检测相互作用关系是昂贵的。因此，网络结构重构是复杂网络系统中的逆问题，对于理解许多具有未知交互结构的复杂系统至关重要。另外，从真实网络系统收集的数据经常被噪声污染，这使得网络结构推断任务更具挑战性。本文基于深度学习方法，为游戏动力学网络结构的重构开发了一个新的框架。与采用计算复杂的凸/贪心算法来解决网络重构任务的压缩感测方法相反，我们引入了一个深度学习框架，该框架可以从节点数据中学习结构化表示，并以很少的观察数据有效地重建游戏动力学网络结构。具体来说，我们提出了降噪自动编码器（DAE）作为无监督特征学习器，以捕获不同节点之间的统计依存关系。与基于压缩感知的方法相比，该方法是一种全局的网络结构推断方法，它不仅可以得到最先进的性能，而且可以直接获得网络的结构。此外，该方法对观测数据中的噪声具有鲁棒性。而且，所提出的方法对于不完全稀疏的网络也是有效的。因此，所提出的方法可以在实践中扩展到网络重建任务的广泛范围。