Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks

TL;DR

This paper introduces a sorting-based method to reorder adjacency matrices of complex networks, enhancing feature extraction and classification accuracy in machine learning tasks.

Contribution

It proposes a novel adjacency matrix sorting technique that improves network classification performance over existing methods.

Findings

Outperforms previous methods on synthetic data

Effective on real-world network datasets

Enhances feature extraction for machine learning

Abstract

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.