Bayesian networks is just another way of saying directed graphical model, which is just a specific way of specifying a generative joint probability distribution.
To get a feeling for the larger field if not for directed graphical models specifically, I’d recommend Richard McElreath’s Statistical Rethinking. It’s more about methodology for how to test model fits, how to do prediction, etc. There’s also Gelman et al.'s “Bayesian Worfklow” (arXiv), but in the sake of full disclosure, I’m a co-author on that one—it’s being expanded into a book of case studies. It’s largely about how to do applied Bayesian modeling and predictive inference. Both of these are based on Stan, not PyMC, but it’s a prettys simple translation between them for most problems.