A simple stochastic process is described which provides a useful basis for generating some types of random networks. The process is based on an iterative building block technique that uses a motif profile as a conditional probability model. The conditional iterative form of the algorithm insures that the calculations required to simulate an observed random network are relatively simple and does not require complicated models to be fit to an observed network. Bounds on the theoretical cohesiveness of the realized networks are established and empirical studies provide indications on more general properties of the resulting network, suggesting the types of applications where the process would be useful. The algorithm is used to generate networks similar to those observed in several examples.

描述了一个简单的随机过程，该过程为生成某些类型的随机网络提供了有用的基础。该过程基于将主题概要用作条件概率模型的迭代构建块技术。该算法的条件迭代形式可确保模拟观察到的随机网络所需的计算相对简单，并且不需要复杂的模型即可适应观察到的网络。建立了有关已实现网络的理论凝聚力的界限，并且经验研究为所得网络的更一般属性提供了指示，建议了该过程将有用的应用程序类型。该算法用于生成类似于在几个示例中观察到的网络。