SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction

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.