Structure learning is essential for Bayesian networks (BNs) as it uncovers causal relationships, and enables knowledge discovery, predictions, inferences, and decision-making under uncertainty. Two novel algorithms, FSBN and SSBN, based on the PC algorithm, employ local search strategy and conditional independence tests to learn the causal network structure from data. They incorporate d-separation to infer additional topology information, prioritize conditioning sets, and terminate the search immediately and efficiently. FSBN achieves up to 52% computation cost reduction, while SSBN surpasses it with a remarkable 72% reduction for a 200-node network. SSBN demonstrates further efficiency gains due to its intelligent strategy. Experimental studies show that both algorithms match the induction quality of the PC algorithm while significantly reducing computation costs. This enables them to offer interpretability and adaptability while reducing the computational burden, making them valuable for various applications in big data analytics.

翻译：结构学习是贝叶斯网络（Bayesian networks, BNs）的关键环节，它不仅能够揭示因果关系，还能支持知识发现、预测、推理以及不确定性条件下的决策制定。本文提出两种基于PC算法的新型算法FSBN和SSBN，通过局部搜索策略和条件独立性检验从数据中学习因果网络结构。它们利用d-分离推断额外的拓扑信息，优先选择条件集，并即时高效终止搜索过程。FSBN最多可降低52%的计算成本，而SSBN在200节点网络上以惊人的72%成本削减幅度进一步超越前者。得益于其智能策略，SSBN展现出更高的效率提升。实验研究表明，两种算法在保持与PC算法同等归纳质量的同时，显著降低了计算成本。这使得它们能够在减轻计算负担的同时提供可解释性和适应性，从而在大数据分析的各类应用中具有重要价值。