Layout
Context
- Machine-learning Bayesian networks can produce very complex structures, which can potentially be difficult to comprehend visually.
- Typically, much of the cognitive challenges can be resolved by properly arranging the nodes in the newly discovered network structure. However, except in trivial cases, this can't realistically be done manually.
- BayesiaLab offers numerous layout algorithms, which can automate the "untangling" of crisscrossing nodes and arcs and quickly produce a comprehensible graph.