标题： 基于约束的贝叶斯结构学习算法的基准测试：网络拓扑的作用 显示英文标题

标题： Benchmarking Constraint-Based Bayesian Structure Learning Algorithms: Role of Network Topology

摘要： 从多变量横断面资料中对现实世界实体之间的关联进行建模，可以提供这些实体作为系统协同工作的线索。 已经提出了多种技术来破译这些关联，包括将它们建模为有向无环图的基于约束的贝叶斯结构学习（BSL）算法。 对这些算法的基准测试通常集中在评估性能指标的变化上，例如敏感性作为由DAG中的节点数表示的维度和样本量的函数。 本研究阐明了网络拓扑在基准测试中的重要性。 更具体地说，它在保持节点、边和样本量相同的情况下，研究了不同网络拓扑之间的敏感性变化，消除了这些因素作为潜在混杂因素的可能性。 研究了三种流行的基于约束的BSL算法（Peter-Clarke、Grow-Shrink、Incremental Association Markov Blanket）在从采样自使用优先连接生成的亚线性、线性和超线性DAG拓扑网络模型的多变量横断面资料中学习网络结构时的敏感性。 在线性和非线性模型中的结果揭示了在三个算法中从亚线性到超线性拓扑的敏感性估计值统计学上显著的$(\alpha=0.05)$下降。 这些结果在具有节点数$(N_{nods}=48,64)$、噪声强度$(\sigma =3,6)$和样本量$(N = 2^{10})$的网络上得到展示。 这些发现阐明了在基于约束的BSL基准测试中考虑网络拓扑的重要性。 显示英文摘要

摘要： Modeling the associations between real world entities from their multivariate cross-sectional profiles can provide cues into the concerted working of these entities as a system. Several techniques have been proposed for deciphering these associations including constraint-based Bayesian structure learning (BSL) algorithms that model them as directed acyclic graphs. Benchmarking these algorithms have typically focused on assessing the variation in performance measures such as sensitivity as a function of the dimensionality represented by the number of nodes in the DAG, and sample size. The present study elucidates the importance of network topology in benchmarking exercises. More specifically, it investigates variations in sensitivity across distinct network topologies while constraining the nodes, edges, and sample-size to be identical, eliminating these as potential confounders. Sensitivity of three popular constraint-based BSL algorithms (Peter-Clarke, Grow-Shrink, Incremental Association Markov Blanket) in learning the network structure from multivariate cross-sectional profiles sampled from network models with sub-linear, linear, and super-linear DAG topologies generated using preferential attachment is investigated. Results across linear and nonlinear models revealed statistically significant $(\alpha=0.05)$ decrease in sensitivity estimates from sub-linear to super-linear topology constitutively across the three algorithms. These results are demonstrated on networks with nodes $(N_{nods}=48,64)$, noise strengths $(\sigma =3,6)$ and sample size $(N = 2^{10})$. The findings elucidate the importance of accommodating the network topology in constraint-based BSL benchmarking exercises.