Evaluating the Natural Variability in Generative Models for Complex Networks
Evaluating the Natural Variability in Generative Models for Complex Networks
复制标题
DOI：
10.1007/978-3-030-05411-3_59
复制
发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
V. Arora;M. Ventresca
中科院分区：
文献类型：
--
作者：
V. Arora;M. Ventresca
复杂网络用于使用分别表示元素及其交互的节点和边集合来表示真实世界的系统。理解这些网络结构(以及产生这些网络结构的过程)的一个原则性方法是制定生成性模型，并从给定的数据中推断出它们的参数。理想情况下，生成性模型应该能够合成与观测数据属于同一群体的网络，但大多数模型都不是为完成这一任务而设计的。由于网络种群形式的数据稀缺，生成模型通常被用来从单个网络观测中学习参数，从而忽略了网络种群的自然变异性。在这篇文章中，我们评估了四个生成模型关于它们合成网络的能力，这些网络与观察到的网络属于同一种群。我们的实证分析量化了网络模型复制一组网络人口特征的能力，突显了重新思考我们评估新的和现有网络模型的拟合优度的方式的必要性。
Complex networks are used to represent real-world systems using sets of nodes and edges that represent elements and their interactions, respectively. A principled approach to understand these network structures (and the processes that give rise to them) is to formulate generative models and infer their parameters from given data. Ideally, a generative model should be able to synthesize networks that belong to the same population as the observed data, but most models are not designed to accomplish this task. Due to the scarcity of data in the form of populations of networks, generative models are typically formulated to learn parameters from a single network observation, hence ignoring the natural variability of network populations. In this paper, we evaluate four generative models with respect to their ability to synthesize networks that belong to the same population as the observed network. Our empirical analysis quantifying the ability of network models to replicate characteristics of a population of networks highlights the need for rethinking the way we evaluate the goodness of fit of new and existing network models.