title: Pagerank Personalization vector , edge weights and dangling dictionary (teleportation vector)
body: <p>This is the pagerank function from networkx</p>
  
  <pre><code>def pagerank(G, alpha=0.85, personalization=None,
               max_iter=100, tol=1.0e-6, nstart=None, weight='weight',
               dangling=None):
  </code></pre>
  
  <p>I am confused with personalization and weight.</p>
  
  <p>I understand the when personalization matrix is not provides a uniform matrix is used and when weight is not provided edge weight of 1 is used.</p>
  
  <p>I have been reading about :Edge weight personalization and Node Weight Personalization.</p>
  
  <p><a href="http://www.cs.cornell.edu/~bindel/present/2015-08-kdd-talk_kdd-aug15.pdf" rel="nofollow">http://www.cs.cornell.edu/~bindel/present/2015-08-kdd-talk_kdd-aug15.pdf</a></p>
  
  <p>So my assumption is personalization vector --> can be used for node weight personalization. for example. we are calculating pagerank of nodes for particular topic t . we give personalization  vector where nodes more relevant to the topic get more value. </p>
  
  <p>But what about edge weight. What happens when I give higher weight to one outgoing edge from V as compared to other. ?</p>
  
  <p>I am not able to segregate these two things (edge weights and personalization vector) .</p>
  
  <p>and also :
  the dictionary dangling : which represents the teleportation vector. If not provided personalization vector is used. The dangling dictionary is more understandable to me , that is provides the probability of random transition when we reach a dangling node.</p>
  
  <p>Could someone help be understand the scenario where I provide all three , i.e edge weight, personalization vector and Dangling vector ( How will pagerank be affected)</p>