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In recent years, band selection is becoming a popular approach to reduce the dimensionality of hyperspectral data while preserving the desired information for target detection and classification analysis. This letter presents a new method for unsupervised band selection by transforming the hyperspectral data into complex networks. By analyzing the networks' topological feature corresponding to each band, one can easily evaluate the statistical characteristics and intrinsic properties of the signals. The proposed method searches for the network set which is most qualified for demarcating and identifying different substance signatures, and then, the network set's corresponding bands are regarded as the descried output results. This network measure is a new criterion for band selection. Experimental results demonstrate that the proposed method can acquire satisfactory results when compared with traditional methods.