Roles of mixing patterns in the network reconstruction
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Updated: Mar 10, 2026

Quantifying Mixing using Magnetic Resonance Imaging
Published on: January 25, 2012
Qiang Guo1, Guang Liang1, Jia-Qi Fu1
1Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
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.
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