网络机制的经验分类
Empirically Classifying Network Mechanisms
摘要：
网络模型用于研究跨许多物理，生物和社会学科的互连系统。这类模型通常假设一种特定的网络生成机制，当与数据拟合时，会产生描述系统如何运行的机制特定参数的估计值。例如，一个社交网络模型可能会假设新的个体与其他人建立联系的概率与他们先前存在的联系数量成正比（“优先依恋”），然后估计具有类似资格的知名和默默无闻的个体之间互动的差异。然而，如果没有一种手段来检验假定机制的相关性，从这些模型得出的结论可能会产生误导。在这里，我们介绍了一个简单的经验方法，可以机械地分类任意网络数据。我们的方法比较了经验网络和来自用户提供的候选机制集的模型网络，并将每个网络（具有高准确性）分类为源自其中一种机制或不源自其中一种机制。我们测试了373个经验网络与五个最广泛研究的网络机制，发现大多数（228）与这些机制不同。这就提出了一些经验网络是由多种机制混合而成的可能性。我们发现，混合物往往是不可识别的，因为不同的混合物可以产生功能等效的网络。在这样的系统中，这是由多种机制，我们的方法仍然可以准确地预测样本外的功能特性。
英文摘要：
Network models are used to study interconnected systems across many physical, biological, and social disciplines. Such models often assume a particular network-generating mechanism, which when fit to data produces estimates of mechanism-specific parameters that describe how systems function. For instance, a social network model might assume new individuals connect to others with probability proportional to their number of pre-existing connections ('preferential attachment'), and then estimate the disparity in interactions between famous and obscure individuals with similar qualifications. However, without a means of testing the relevance of the assumed mechanism, conclusions from such models could be misleading. Here we introduce a simple empirical approach which can mechanistically classify arbitrary network data. Our approach compares empirical networks to model networks from a user-provided candidate set of mechanisms, and classifies each network--with high accuracy--as originating from either one of the mechanisms or none of them. We tested 373 empirical networks against five of the most widely studied network mechanisms and found that most (228) were unlike any of these mechanisms. This raises the possibility that some empirical networks arise from mixtures of mechanisms. We show that mixtures are often unidentifiable because different mixtures can produce functionally equivalent networks. In such systems, which are governed by multiple mechanisms, our approach can still accurately predict out-of-sample functional properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
