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.

基于 PC 算法的 FSBN 和 SSBN 算法使用局部搜索策略和条件独立性测试从数据中学习因果网络结构，通过引入 d - 分离来推断更多的拓扑信息，优先级调整条件集，并且能够立即和高效地终止搜索，从而实现了高达 52%（FSBN）和 72%（SSBN）的计算成本降低，对于 200 个节点的网络 SSBN 表现出更高的效率，实验证明这两个算法在减少计算成本的同时能够保持与 PC 算法相同的归纳质量，适用于大数据分析的各种应用。