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The networks were evaluated by 4 distinct criteria:
The first score shall be maximized, the other scores shall be minimized. In order to obtain the scale, your results are compared with the original network (denoted as orig) and a random network (denoted as random, its structure was learned by K2 having a random variable order with an extremely small sample size, its parameters were randomized). On top of that, there are two straightforwardly learned networks K2Random (K2 algorithm, a random order of variables, properly learned parameters) and K2correct (K2 algorithm, the correct order of variables, properly learned parameters).
I ranked your solutions in descending order, I used a weighted average of loglik, size and totVarDist. The orig network (almost necessarily) wins. At the same time, there are at least 6 proper solutions that approach the original. Other 10 solutions can be concerned as very good. The remaining models score poorly. On one hand, the cause can be a minor inattention, on the other hand, you were supposed to check before submission.