Aggregate Ranking using Khatri-Rao product
Rank both graphs separately each node gets a rank in both graphs then do simple matrix addition. Now normalize the rank. This should keep the relationship like rank1>rank2>rank3>rank4 true and relationships like rank1+rank1>rank1+rank2 true. I don't know how it would help you taking the Khatri-Rao product of the matrix. That would make you end up with more than 400 nodes. Then you would need to compress them back to 400 nodes in-order to have 400 ranked nodes at the end. Who told you to use Khatri-Rao product?
I have constructed 2 graphs and calculated the eigenvector centrality of each node. Each node can be considered as an individual project contributor. Consider 2 different rankings of project contributors. They are ranked based on the eigenvector of the node. 
Ranking #1:
Rank 1 - A
Rank 2 - B
Rank 3 - C
Ranking #2:
Rank 1 - B
Rank 2 - C
Rank 3 - A
This is a very small example but in my case, I have almost 400 contributors and 4 different rankings. My question is how can I merge all the rankings and get an aggregate ranking. Now I can't just simply add the eigenvector centralities and divide it by the number of rankings. I was thinking to use the Khatri-Rao product or Kronecker Product to get the result. 
Can anyone suggest me how can I achieve this?
Thanks in advance.