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 300 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：二部网络的统计分析通常需要从与观测网络具有相同度数序列的所有二部网络集合中随机抽样。交易算法通过逐步“交易”部分边的位置，提供了一种生成二部网络样本的高效方法。然而，如何确定所需的交易次数以确保样本的随机性仍是一个难题。本文提出了一种停止规则，该规则聚焦于抽样网络与观测网络之间的距离，并在该分布稳定时停止交易。分析表明，针对超过300种不同的度数序列，使用该停止规则能以高概率确保随机样本，且该规则在实际应用中具有可行性。