Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural features. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. Within each such class, networks describing similar systems tend to have similar features. This occurs presumably because networks representing similar systems would be expected to be generated by a shared set of domain-specific mechanisms, and it should therefore be possible to classify networks based on their features at various structural levels. Here we describe and demonstrate a new hybrid approach that combines manual selection of network features of potential interest with existing automated classification methods. In particular, selecting well-known network features that have been studied extensively in social network analysis and network science literature, and then classifying networks on the basis of these features using methods such as random forest, which is known to handle the type of feature collinearity that arises in this setting, we find that our approach is able to achieve both higher accuracy and greater interpretability in shorter computation time than other methods.

中文翻译：

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EndNote：基于特征的网络分类

来自不同科学和社会领域的系统的网络表示既不是完全随机的，也不是完全规则的，而是似乎包含重复出现的结构特征。这些特征往往由属于同一大类的网络共享，例如社交网络类或生物网络类。在每个此类中，描述相似系统的网络往往具有相似的特征。发生这种情况大概是因为代表相似系统的网络预计将由一组共享的特定领域机制生成，因此应该可以根据网络在不同结构级别的特征对网络进行分类。在这里，我们描述并演示了一种新的混合方法，该方法将手动选择潜在感兴趣的网络特征与现有的自动分类方法相结合。特别是，选择在社会网络分析和网络科学文献中广泛研究的众所周知的网络特征，然后使用随机森林等方法根据这些特征对网络进行分类，随机森林已知可以处理特征共线性的类型在这种情况下出现的情况，我们发现我们的方法能够在比其他方法更短的计算时间内实现更高的准确性和更大的可解释性。