Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections.
Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections.
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DOI：
10.1038/s41598-021-03238-3
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发表时间：
2021-12-14
影响因子：
4.6
通讯作者：
Sagan B
中科院分区：
文献类型：
--
作者：
Neal ZP;Domagalski R;Sagan B
二分或双模式网络的投影捕获同现，并且用于不同的领域（例如，生态学、经济学、文献计量学、政治学）来表示单分网络。分析此类网络的一个关键挑战是确定观察到的两个节点之间的同现次数是否很大，从而确定它们之间是否存在边。一种方法，固定度序列模型（FDSM），通过与原二分网络的度序列固定的空模型进行比较来评估边的权重的重要性。虽然FDSM是一个直观的零模型，它是计算昂贵的，因为它需要蒙特卡罗模拟来估计每个边缘的p值，因此是不切实际的大投影。在本文中，我们探讨了四个潜在的替代FDSM：固定填充模型，固定行模型，固定列模型，和随机度序列模型（SDSM）。我们比较这些模型的准确性，速度，统计能力，相似性和恢复已知社区的能力。我们发现，计算速度快的SDSM提供了一个统计上保守的，但密切的近似计算不切实际的FDSM在广泛的条件下，它正确地恢复一个已知的社区结构，即使当信号很弱。因此，虽然每个骨干模型可能有特定的应用程序，我们建议SDSM提取骨干的二分投影FDSM是不切实际的。
Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.
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