Abstract: To uncover the networks' structure according to limited interactional information is one of the significant problems in the field of network science. We develop a method for reconstructing and analyzing the networks' structure based on single structure attribute of network. First, a series of synthetic networks with tunable clustering coefficient are generated under Holme-Kim model. Then, the networks' structure is reconstructed by virtue of a compressive-sensing identification model with limited information. Experimental results demonstrate that when we have 20% time-series information of interactions between nodes in the networks, both the average identification accuracy and the average recall of existent links of the whole networks would be increased with the increment of the average clustering coefficient. In this paper, the average clustering coefficient of the target networks is varying from 0.1 to 0.6. The average identification accuracy and the average recall of existent links would reach the optimum value when the network's average clustering coefficient is 0.6. Further, we make a deeper investigation into the experimental data. We find that the average identification accuracy of the networks largely depends on that of the corresponding nodes, whose degree is less than 8 in networks.