# Estimate the probability of missing links based on partially observed graphs

Suppose the underlying true graph $$G^T$$ is generated from some known random graph model. We are able to obverse a partial graph $$G^O$$ (Assume the difference between $$G^T$$ and $$G^O$$ is that some links are missing in $$G^O$$).

Based on this $$G^O$$ and our knowledge about the random graph model, can we estimate the likelihood of links among each pair of nodes in $$G^O$$? I wonder if there is a systematical way/related research to treat this problem.

I am aware that there are some techniques like link prediction to predict missing links; but it only uses the topology information of $$G^O$$ without the knowledge of the random graph model.

• This question is far too vaguely worded to get a good answer. How did you arrive at $G^O$ in the first place? – Mike Nov 1 '18 at 19:33