Goyal Ravi, De Gruttola Victor, Onnela Jukka-Pekka
Division of Infectious Diseases and Global Public, Health, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA.
Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA USA.
J Complex Netw. 2023 Oct 20;11(5):cnad034. doi: 10.1093/comnet/cnad034. eCollection 2023 Oct.
There are two prominent paradigms for the modelling 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 (models developed based on the mechanistic approach) are appealing because they capture scientific processes that are believed to be responsible for network generation; however, they do not easily lend themselves to the use of inferential techniques when compared with probabilistic models. We introduce a general framework for converting a mechanistic network model (MNM) to a probabilistic network model (PNM). The proposed framework makes it possible to identify the essential network properties and their joint probability distribution for some MNMs; doing so makes it possible to address questions such as whether two different mechanistic models generate networks with identical distributions of properties, or whether a network property, such as clustering, is over- or under-represented in the networks generated by the model of interest compared with a reference model. The proposed framework is intended to bridge some of the gap that currently exists between the formulation and representation of mechanistic and PNMs. We also highlight limitations of PNMs that need to be addressed in order to close this gap.
第一种称为机制方法，即指定一组特定领域的机制规则，用于随着时间的推移来生长或演化网络；第二种称为概率方法，即描述一个模型，该模型指定观察给定网络的可能性。机制模型（基于机制方法开发的模型）很有吸引力，因为它们捕捉了被认为是网络生成原因的科学过程；然而，与概率模型相比，它们不容易应用推理技术。我们引入了一个将机制网络模型（MNM）转换为概率网络模型（PNM）的通用框架。所提出的框架使得识别某些MNM的基本网络属性及其联合概率分布成为可能；这样做使得能够解决诸如两个不同的机制模型是否生成具有相同属性分布的网络，或者与参考模型相比，感兴趣的模型生成的网络中诸如聚类等网络属性是否被过度或不足表示等问题。所提出的框架旨在弥合目前机制模型和PNM在制定和表示方面存在的一些差距。我们还强调了为弥合这一差距而需要解决的PNM的局限性。
