title: Roles of mixing patterns in the network reconstruction.
abstract: Compressive sensing is an effective way to reconstruct the network structure. In this paper, we investigate the effect of the mixing patterns, measured by the assortative coefficient, on the performance of network reconstruction. First, we present a model to generate networks with different assortativity coefficients, then we reconstruct the network structure by using the compressive sensing method. The experimental results show that when the assortativity coefficient r=0.2, the accuracy of the network reconstruction reaches the maximum value, which suggests that the compressive sensing is more effective for uncovering the links of social networks. Moreover, the accuracy of the network reconstruction will be higher as the network size increases.
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Guo
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
authors.forename: Guang
authors.initials: G
authors.lastname: Liang
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
authors.forename: Jia-Qi
authors.initials: JQ
authors.lastname: Fu
authors.affiliation: Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, People's Republic of China.
authors.forename: Jing-Ti
authors.initials: JT
authors.lastname: Han
authors.affiliation: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.|Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, People's Republic of China.
authors.forename: Jian-Guo
authors.initials: JG
authors.lastname: Liu