Applied Bayesian Networks Rely on Expert Knowledge and Scarce Data Sharing

To assess how Bayesian network structures are built and learned in applied work, we screened 5,993 recent papers (2020-2025) whose abstracts mention “Bayesian (belief) network” and deemed 3,661 relevant. Among these relevant papers, expert knowledge was used in 2,059 papers (56.2%): 1,785 (48.8%) relied on expert knowledge alone, whereas 274 (7.5%) combined expert input with algorithmic structure learning through edge modification or structural constraints. Data sharing was scarce: only 129 studies (3.5%) provided functional dataset links, which we curated into an open benchmark index. These findings support a data-first norm: share datasets, prefer automatic structure-learning baselines, and document expert input. Among 1,106 papers (30.2%) using algorithms without expert knowledge, score-based flags were most common (797, 72.1%; mainly hill climbing, K2, and tabu), followed by constraint-based methods (194, 17.5%; mainly PC and Grow-Shrink), fixed or restricted-topology BN classifiers (143, 12.9%; mainly TAN and naive Bayes), and hybrid methods (131, 11.8%; mainly MMHC); bootstrapping appeared in 223 papers (6.1%). Reported practice remains concentrated around familiar algorithms.