Importance of Vertices in Complex Networks Applied to Texture Analysis

Discriminating Power of Centrality Measures in Complex Networks

Centrality metrics are one of the most fundamental tools in social network analysis and network science, and various measures for evaluating node importance metrics have been devised. However, the crucial issue of testing the discriminating power of different centrality measures is still open. In this article, we propose to assess the discriminating power of node centrality measures by using the notion of automorphism and orbit: nodes in the same orbit have identical metric scores, while nodes in different orbits should have different centrality values. Under this assumption, we present a benchmark for the discriminating power of node centrality measures. Moreover, we propose an efficient approach to evaluate centrality measures in terms of the discriminating power, which is devoid of finding orbits. Extensive experiments on real and model networks are executed to compare seven commonly used node centrality metrics.

A Distributed System-Based Multiplex Networks to Extract Texture Feature

Defect detection is an indispensable part of quality detection in manufacturing. It is a challenging task to recognize defects on the surface of castings with random textures. This paper proposes a texture extraction method based on multiplex networks for defect segmentation in a random background. The proposed method redefines the image information in the form of multiplex network topologies according to the different properties of casting surface texture. Finally, the proposed method segments different texture regions by extracting the similarity of texture primitives in the multiplex networks. The study conducted experiments in a distributed system environment, and the results show that the proposed method is effective in actual industrial data sets. As an interdisciplinary application of network science and machine vision, the proposed method provides a valuable application mode for the development of complex networks in new fields and provides a new research idea for the texture analysis of castings.