CCMnet: 基于同余类模型的网络生成软件包
CCMnet: A Software Package for Network Generation with Congruence Class Models
AI总结：
提出CCMnet R包，通过同余类上的概率分布实现网络属性的连续可变性，支持MCMC采样，用于生成反映实证数据不确定性的网络集成。
摘要：
我们介绍CCMnet，一个R包，旨在生成准确反映实证数据固有不确定性的网络集成。虽然传统网络建模通常产生具有固定属性值或模型确定变异水平的集成，但CCMnet允许网络属性（包括边数、度分布和混合模式）具有连续的变异谱。通过直接在网络的同余类上定义概率分布，该包允许研究人员指定生成集成中网络属性的不确定性，以匹配特定的抽样设计或实证分布。此外，这一公式提供了一个原则性框架，涵盖了几种经典模型（例如Erdős–Rényi模型、随机块模型和某些指数随机图模型），这些模型隐含地共享这一结构基础，同时提供了为网络属性指定任意（甚至非参数）分布的灵活性。CCMnet实现了马尔可夫链蒙特卡洛（MCMC）框架来从这些模型中采样。通过生成代表学校友谊网络的后验预测网络集成，展示了该包的实用性。
英文摘要：
We introduce CCMnet, an R package designed to generate network ensembles that accurately reflect the uncertainty inherent in empirical data. While traditional network modeling often results in ensembles with fixed property values or model-determined levels of variability, CCMnet enables a continuous spectrum of variability for network properties, including edge counts, degree distribution, and mixing patterns. By defining probability distributions directly over congruence classes of networks, the package allows researchers to specify the uncertainty in network properties across the generated ensemble to match a specific sampling design or empirical distribution. Furthermore, this formulation provides a principled framework that encompasses several classic models (e.g., Erdős--Rényi model, stochastic block models, and certain exponential random graph models) that implicitly share this structural basis, while offering the flexibility to specify arbitrary, even non-parametric, distributions for network properties. CCMnet implements a Markov chain Monte Carlo (MCMC) framework to sample from these models. The utility of the package is illustrated by generating posterior predictive network ensembles representing school friendship networks.
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。
