How can I associate centrality score of genes(vertices) in a GRN with the probabilistic model in terms of cause and effect?

Question

I'm learning to build Bayesian Network Based Modeling of Gene Regulatory Network of Cancerous Cellular Cells. So far, I have learned to form a Bayesian Network based on nature of genes(i.e Functional Genes and TFs). However, I cannot figure out how to give probability to each node/vertex. Is it possibile to somehow iterpret the centrality score of each vertex an associate the meaning with the probabilistic model in terms of cause and effect?

or can you atleast refer me a relevant research paper?

I believe I would be worth mentioning that I'm fairly new to this.

Here's my data of TFs and Sources.

TF	Target	Model of Regulation	References
ABL1	BCL2	Repression	11753601
AHR	FOS	Unknown	10478842
APC	BIRC5	Unknown	11751382
AR	ERBB2	Activation	21613411
AR	ERBB2	Unknown	21741601

Following is the sample of my data for network analysis, mostly containing centrality scores some graph properties.

SUID	name	BetweennessCentrality	ClosenessCentrality	ClusteringCoefficient	Eccentricity	EdgeCount	Indegree	IsSingleNode	NeighborhoodConnectivity	Outdegree	PartnerOfMultiEdgedNodePairs	selected	SelfLoops	shared name	Stress
7228	TP53	0.06611272	0.46456693	0.04141865	4	75	50	FALSE	14.40625	25	11	FALSE	0	TP53	39572
6788	AR	0.03605041	0.40410959	0.03164983	4	60	46	FALSE	13.38181818	14	5	FALSE	0	AR	20498
7029	MYC	0.03342125	0.35014837	0.03280053	5	81	74	FALSE	12.93589744	7	3	FALSE	0	MYC	19140