Community detection has been one of the central problems in network studies and
directed network is particular challenging due to asymmetry  among its links.
In
this talk, we discuss incorporating the direction of links reveals new
perspective on communities regarding to two different roles, source and
terminal. Intriguingly, such communities appear to be connected with unique
spectral property of the graph Laplacian of the adjacency matrix and we
exploit
this connection by using regularized SVD methods. We propose harvesting
algorithms, coupled with regularized SVDs, that are linearly scalable for
efficient identification of communities in huge directed networks.