用随机游走统计量识别网络类型，效果优于多数现有方法。
Network classification through random walks
- 通过随机游走的统计特征提取网络结构信息
- 在多个数据集上分类准确率优于主流方法
- 适合网络分类与结构分析任务的研究者使用
网络模型被广泛用于研究各类系统及其动态行为。面对网络结构的多样性，一个关键问题浮现：能否仅根据网络结构推断其所代表的系统类型？该分类任务依赖于从网络中提取有效特征。已有研究提出结合结构度量与动态过程的方法进行特征提取。本文提出一种新方法，利用随机游走的统计特性来表征网络，这些统计量能有效反映网络属性。我们定义并测试了若干统计指标，并在多个数据集上将其性能与当前最优特征提取方法进行对比。结果表明，该方法在多数情况下表现优异，常超越现有方法，但在某些数据集上仍存在局限性。
原文摘要 · Abstract (English)
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.
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