Complex network's topological similarity analysis based on spectral density

Spectral density of complex network can reflect network's structural properties. After the comparison of WS `small-world' networks, ER random networks, BA `scale-free' networks and CNN networks with different parameters and scales, the shapes of spectral density curves perform very strong clustering features. Based on these results, this paper proposes a novel method analyzing the similarity of network topology based on the shapes of spectral density curves. The specific definition and algorithm of similarity indices are also given. The topological similarity among model networks, real networks and their subnets is analyzed by the proposed method. Experiments results on both model networks and real networks verify the effectiveness and feasibility of the proposed method.