I am trying to estimate the graph in very high dimensional data, I mean with million nodes. Up to now all the papers that I have found, they are limited to few thousands.
All of them like graphical lasso, non parnormal, they use the estimated covariance matrix and then use the gaussian likelihood function which they optimize to find the precision matrix, which actually encodes the graph structure
In my case, I have million nodes. So if I try to estimate the covariance matrix at the beginning, that is a dense matrix of 1 million x 1 million. I will run out of memory with that
I wanted to know if any methods have been devised that actually deal with this issue. Or this problem is unsolved?