Yassin Ali, Cherifi Hocine, Seba Hamida, Togni Olivier
LIB, Université de Bourgogne, Franche-Comté, Dijon, France.
UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, Univ Lyon, Villeurbanne, France.
PLoS One. 2025 Jan 3;20(1):e0316141. doi: 10.1371/journal.pone.0316141. eCollection 2025.
The backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives. Interestingly, ordering the other methods from GloSS to NC, PF, LANS, and MLF, we observe that each method's output encapsulates the backbone of the previous one. Correlation analysis between edge features (weight, degree, betweenness) and the test significance level reveals that the DF and LANS filters favor high-weighted edges while ECM assigns them lower significance to edges with high degrees. Furthermore, the results suggest a limited influence of the edge betweenness on the filtering process. The backbones global properties analysis (edge fraction, node fraction, weight fraction, weight entropy, reachability, number of components, and transitivity) identifies three typical behavior types for each property. Notably, the LANS filter preserves all nodes and weight entropy. In contrast, DF, PF, ECM, and GloSS significantly reduce network size. The MLF, NC, and ECM filters preserve network connectivity and weight entropy. Distribution analysis highlights the PU filter's ability to capture the original weight distribution. NC filter closely exhibits a similar capability. NC and MLF filters excel for degree distribution. These insights offer valuable guidance for selecting appropriate backbone extraction methods based on specific properties.
骨干提取过程对于加快网络应用中的分析和增强可视化至关重要。本研究系统地比较了七种有影响力的统计假设检验骨干边过滤方法（差异过滤器（DF）、波利亚瓮过滤器（PF）、边际似然过滤器（MLF）、噪声校正（NC）、增强配置模型过滤器（ECM）、全局统计显著性过滤器（GloSS）和局部自适应网络稀疏化过滤器（LANS））在不同网络中的表现。相似性分析表明，使用ECM和DF过滤器提取的骨干与从其他替代方法派生的骨干重叠最小。有趣的是，将其他方法从GloSS到NC、PF、LANS和MLF排序，我们观察到每种方法的输出都包含了前一种方法的骨干。边特征（权重、度、介数）与测试显著性水平之间的相关性分析表明，DF和LANS过滤器倾向于高权重边，而ECM对高度边赋予较低的显著性。此外，结果表明边介数对过滤过程的影响有限。骨干全局属性分析（边分数、节点分数、权重分数、权重熵、可达性、组件数量和传递性）为每个属性确定了三种典型行为类型。值得注意的是，LANS过滤器保留了所有节点和权重熵。相比之下，DF、PF、ECM和GloSS显著减小了网络规模。MLF、NC和ECM过滤器保留了网络连通性和权重熵。分布分析突出了PU过滤器捕获原始权重分布的能力。NC过滤器也表现出类似的能力。NC和MLF过滤器在度分布方面表现出色。这些见解为根据特定属性选择合适的骨干提取方法提供了有价值的指导。
