Network classification through random walks

TL;DR

This paper introduces a new method for classifying networks by analyzing statistics derived from random walks, which effectively captures network properties and often outperforms existing feature extraction techniques.

Contribution

The study proposes a novel random walk-based feature extraction approach for network classification, demonstrating its effectiveness across multiple datasets.

Findings

The method often outperforms existing approaches.

It effectively captures network structural properties.

Some limitations are observed on certain datasets.

Abstract

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.