Dirichlet distribution

I have a directed graph with the label. can you explain to me how to compute Prior Dirichlet for each node? I need to use the Prior Dirichlet in MAP. I keen to use it in my work.

Unfortunately I don’t understand what it is you’re trying to do and what your issue is.

I will use a GMM model in my work and I am going to use MAP for linkprediction in GMM model. the MAP is prior*likelihood and i compute Dirichlet for prior and likelihood. i am doing this beacause last papers only count the number of edges and it leads to eliminate some of nodes.
is it possible to use Dirichlet just for Prior?? can i use a kernel similarity for likelihood?? I know that if i use this method, the type of prior and likelihood are not same, but I am not sure it work truly for MAP.