Graph structure analysis based on complex network

Authors:
Jin Tang;Bo Jiang;Chin-Chen Chang;Bin Luo
Affiliations:
School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China and Key lab of Image Processing & Analysis of Anhui Province, Hefei 230039, Anhui, China;School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China;Department of Information Engineering and Computer Science, Feng Chia University, Taiwan;School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China
Venue:
Digital Signal Processing
Year:
2012

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Abstract

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.