Psychology Department, Michigan State University, United States of America.
PLoS One. 2024 May 10;19(5):e0302973. doi: 10.1371/journal.pone.0302973. eCollection 2024.
Bipartite projections (e.g., event co-attendance) are often used to measure unipartite networks of interest (e.g., social interaction). Backbone extraction models can be useful for reducing the noise inherent in bipartite projections. However, these models typically assume that the bipartite edges (e.g., who attended which event) are unconstrained, which may not be true in practice (e.g., a person cannot attend an event held prior to their birth). We illustrate the importance of correctly modeling such edge constraints when extracting backbones, using both synthetic data that varies the number and type of constraints, and empirical data on children's play groups. We find that failing to impose relevant constraints when the data contain constrained edges can result in the extraction of an inaccurate backbone. Therefore, we recommend that when bipartite data contain constrained edges, backbones be extracted using a model such as the Stochastic Degree Sequence Model with Edge Constraints (SDSM-EC).
二部图投影（例如，事件共同出席）通常用于测量有向网络的兴趣（例如，社会互动）。骨干提取模型对于减少二部图投影中固有的噪声非常有用。然而，这些模型通常假设二部边（例如，谁参加了哪个事件）不受约束，但在实践中可能并非如此（例如，一个人不能参加在他们出生之前举行的事件）。我们使用同时包含不同数量和类型约束的合成数据以及有关儿童游戏小组的经验数据来说明在提取骨干时正确建模这种边约束的重要性。我们发现，当数据包含受约束的边时，如果不施加相关约束，可能会导致骨干提取不准确。因此，我们建议当二部图数据包含受约束的边时，使用带约束边的随机度序列模型（SDSM-EC）等模型来提取骨干。
