The use of complex networks as a modern approach to understanding the world and its dynamics is well-established in literature. The adjacency matrix, which provides a one-to-one representation of a complex network, can also yield several metrics of the graph. However, it is not always clear that this representation is unique, as the permutation of lines and rows in the matrix can represent the same graph. To address this issue, the proposed methodology employs a sorting algorithm to rearrange the elements of the adjacency matrix of a complex graph in a specific order. The resulting sorted adjacency matrix is then used as input for feature extraction and machine learning algorithms to classify the networks. The results indicate that the proposed methodology outperforms previous literature results on synthetic and real-world data.

翻译：利用复杂的网络作为了解世界及其动态的现代方法,在文献中已牢固确立; 提供复杂网络一对一表示的相邻矩阵,也可以产生图形的若干量度; 然而,由于矩阵中的线条和行的变换可以代表同一图,这种表示并非始终清楚,因为矩阵中的线条和行的变换可以代表同一图; 为解决这一问题,拟议方法采用一种分类算法,按具体顺序重新排列复杂图表的相邻矩阵的元素; 由此产生的相邻矩阵随后用作特征提取和机器学习算法的输入,以对网络进行分类; 结果表明,拟议的方法优于以前关于合成和现实世界数据的文献结果。