# Network classification through random walks

arXiv: https://arxiv.org/abs/2505.21706 · HTML: https://mlsanity.com/paper/2505.21706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 11 of 11)

- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Random Walks: A Review of Algorithms and Applications](https://mlsanity.com/paper/2008.03639/) (2020)
- [Random walks and diffusion on networks](https://mlsanity.com/paper/1612.03281/) (2016)
- [Random walks on hypergraphs](https://mlsanity.com/paper/1911.06523/) (2019)
- [Complex Network Classification with Convolutional Neural Network](https://mlsanity.com/paper/1802.00539/) (2018)
- [Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks](https://mlsanity.com/paper/2301.08364/) (2023)
- [Characteristic times of biased random walks on complex networks](https://mlsanity.com/paper/1307.3430/) (2013)
- [Random walk centrality in interconnected multilayer networks](https://mlsanity.com/paper/1506.07165/) (2015)
- [Fast deterministic tourist walk for texture analysis](https://mlsanity.com/paper/1611.08624/) (2016)
- [Using deterministic tourist walk as a small-world metric on Watts-Strogatz networks](https://mlsanity.com/paper/2301.08956/) (2023)

## Abstract (arXiv)

Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introduce a novel approach to characterize networks using statistics from random walks, which can be particularly informative about network properties. We present the employed statistical metrics and compare their performance on multiple datasets with other state-of-the-art feature extraction methods. Our results demonstrate that the proposed method is effective in many cases, often outperforming existing approaches, although some limitations are observed across certain datasets.