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

arXiv: https://arxiv.org/abs/2301.08364 · HTML: https://mlsanity.com/paper/2301.08364/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 2 of 2)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Authorship Attribution Based on Life-Like Network Automata](https://mlsanity.com/paper/1610.06498/) (2016)

## Built on by (most cited first, 3 of 3)

- [Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction](https://mlsanity.com/paper/2503.00860/) (2025)
- [Network classification through random walks](https://mlsanity.com/paper/2505.21706/) (2025)
- [Knowledge Distillation for Variational Quantum Convolutional Neural Networks on Heterogeneous Data](https://mlsanity.com/paper/2509.16699/) (2025)

## Abstract (arXiv)

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.