title: Mimicking an existing graph with barabasi-albert model
body: <p>I am currently working on a project that asks to mimic a graph with data taken from past Facebook network. For details it was taken from this site: <a href="https://snap.stanford.edu/data/egonets-Facebook.html" rel="nofollow noreferrer">https://snap.stanford.edu/data/egonets-Facebook.html</a> </p>
  
  <p>Now I want to construct a random graph based on Barabasi-Albert model
  ( <a href="https://en.wikipedia.org/wiki/Barab%C3%A1si%E2%80%93Albert_model" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Barab%C3%A1si%E2%80%93Albert_model</a> ) but with properties derived from the Facebook data (e.g. number of nodes, number of edges, clustering coefficient) </p>
  
  <p>In order to construct the random graph we need to input 4 parameter, which are initial number of nodes (M0), the number of edges that we want to add in each iteration (mlinks), the initial graph with M0 nodes (G0), and the final number of nodes that we want to attain (m).</p>
  
  <p>How can I derive these parameter from the given data, especially the value of M0 and  mlinks (since they will affect the properties of the final graph the most) ? Is there any algorithm, built-in function, or formula that I can use? </p>
  
  <p>Ps. I am working with matlab for this project.</p>