Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks
This addresses a representation issue for researchers analyzing complex networks with machine learning, but appears incremental as it builds on existing sorting and classification approaches.
The paper tackles the problem of non-unique adjacency matrix representations in complex networks by sorting matrix elements in a specific order before feature extraction and classification, reporting improved performance over previous literature on synthetic and real-world data.
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.