Improve scalability of centrality computation using sparse matrix

To enable computation of eigenvector centrality scores for all nodes, in a graph with more than just a few thousand nodes, I have used sparse matrix representation here. Please take a look.

A note:

- For the choice of 'k', refer to 
http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigs.html

k=6 is the default