<ul><li>This paper presents the Greedy Min-Cut Bayesian Consensus (GMCBC) algorithm for the structural fusion of Bayesian Networks (BNs).</li><li>GMCBC integrates principles from flow network theory into BN fusion, adapting the Backward Equivalence Search (BES) phase of the Greedy Equivalence Search (GES) algorithm and applying the Ford-Fulkerson algorithm for minimum cut analysis.</li><li>Experimental results on synthetic Bayesian Networks demonstrate that GMCBC achieves near-optimal network structures.</li><li>In federated learning simulations, GMCBC produces a consensus network that improves structural accuracy and dependency preservation compared to the average of the input networks, resulting in a structure that better captures the real underlying (in)dependence relationships.</li></ul>

Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut Analysis

Discover more