Network classification through random walks

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 an 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. • Proposes a novel set of network features based on random walk statistics. • Achieves top classification accuracy in 10 out of 12 datasets. • Demonstrates strong robustness to structural noise. • Highlights self-avoiding walk visit stats as a lightweight, efficient classifier. • Validated across diverse domains (synthetic, metabolic, bioinformatics, social).