论文标题
将机械网络模型转换为概率模型的框架
Framework for Converting Mechanistic Network Models to Probabilistic Models
论文作者
论文摘要
网络建模有两个突出的范例：在第一个被称为机械方法中，一个指定了一组特定领域的机械规则，这些规则用于随着时间的流逝而用于增长或发展网络；在第二个称为概率方法的第二个中，一个人描述了一个模型，该模型指定了观察给定网络的可能性。机械模型是可扩展的，在某些情况下，允许其某些特性进行分析解决方案，而概率模型具有可用的推论工具。机械模型之所以吸引人，是因为它们捕获了科学过程，这些过程被认为是负责网络的原因。我们引入了一个通用框架，用于将机械网络模型转换为概率网络模型。所提出的框架使能够识别机械网络模型的基本网络属性及其共同概率分布，这可以解决诸如两个机械模型是否生成具有相同感兴趣属性分布的网络，或者与参考模型相比，是否过多或群集属性（例如群集）（例如群集）。所提出的框架旨在弥合机械和概率网络模型之间当前存在的一些差距。
There are two prominent paradigms to the modeling of networks: in the first, referred to as the mechanistic approach, one specifies a set of domain-specific mechanistic rules that are used to grow or evolve the network over time; in the second, referred to as the probabilistic approach, one describes a model that specifies the likelihood of observing a given network. Mechanistic models are scalable and, in select cases, allow for analytical solutions for some of their properties, whereas probabilistic models have inferential tools available. Mechanistic models are appealing because they capture scientific processes that are hypothesized to be responsible for network generation. We introduce a generic framework for converting a mechanistic network model to a probabilistic network model. The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for mechanistic network models, which enables addressing questions such as whether two mechanistic models generate networks with identical distributions of properties of interest, or whether a network property, such as clustering, is over- or under- represented in the generated networks compared to a reference model. The proposed framework is intended to bridge some of the gap that currently exists between mechanistic and probabilistic network models.
