Graph structure analysis based on complex network

In this paper, we propose a novel method to characterize graph structures based on complex network model. First, we show that a structural graph can be modeled as a small-world complex network, and, then, Complex Network Characteristics (including topological and dynamic characteristics) Representation of a Graph (CNCRG) is obtained. Based on these characteristics, graph classification/clustering for objects viewed from different directions and characteristic views identification for single objects are investigated on one synthetic image dataset and two real image datasets. Our experimental results showed that CNCRG achieves better object classification/clustering performance and also provides well-structured view spaces based on multi-dimensional scaling (MDS) and principal component analysis (PCA) embedding methods for graphs extracted from 2D views of 3D objects.