This era of data explosion offers unprecedented opportunities for researchers to investigate and understand scientific questions at a much higher resolution. Particularly, it is of significant importance to further advance our understanding of large-scale networks under a variety of conditions. However, for such challenging problems, the dimension of variables is ultra-high (e.g., tens of thousands or even millions) such that the very standard analyses could become unsuitable, due to the curse of dimensionality. In this study, we concern networks of a size on the order of $10^4$ and the number of parameters on the order of millions. While a few previous computational studies have claimed success in revealing network structures from time-course data, recent work suggests that these methods still suffer from the curse of dimensionality as network size increases to 100 or higher. We thus investigate the sparsity structure of real large-scale networks and propose a novel scalable algorithm for identifying complex network structures, and the highlight of our method is that it can achieve a superior performance even for a network size O($10^4$).