title: Singular Value Decomposition - Social Network Analysis
body: <p>I have a very large network of nodes represented by an adjacency matrix. I would like to reduce the amount of nodes in the network to include the more important nodes. I am aware that SVD can help me achieve this and I have used the ILNumerics library to run the svd() method on the adjacency matrix.</p>
  
  <p>Could someone explain simply to me how the output is mean to help me reduce the dimensions of my network? The SVD process leaves me with a matrix of the same size with descending values diagonally ranging from ~2 to many 0s. How do I know which dimensions to remove which are deemed unimportant?</p>
  
  <p>I am likely doing this process incorrectly overall so any help would be greatly appreciated! Many of the explanations online get very confusing very quickly.</p>