  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.
