Statistical analysis of bipartite networks frequently requires randomly sampling from the set of all bipartite networks with the same degree sequence as an observed network. Trade algorithms offer an efficient way to generate samples of bipartite networks by incrementally `trading' the positions of some of their edges. However, it is difficult to know how many such trades are required to ensure that the sample is random. I propose a stopping rule that focuses on the distance between sampled networks and the observed network, and stops performing trades when this distribution stabilizes. Analyses demonstrate that, for over 650 different degree sequences, using this stopping rule ensures a random sample with a high probability, and that it is practical for use in empirical applications.

翻译：二分图的统计分析通常需要从与观测网络具有相同度序列的所有二分图集合中进行随机抽样。交易算法通过逐步“交易”部分边位置的方式，为生成二分图样本提供了一种高效方法。然而，人们难以确定需要执行多少次交易才能保证样本的随机性。本文提出了一种基于采样网络与观测网络之间距离的终止规则，当该距离分布趋于稳定时停止交易。分析表明，对于超过650种不同的度序列，采用该终止规则能以高概率确保样本的随机性，且在实际应用中具有可行性。