  Community detection has been one of the central problems in network studies
and directed network is particularly challenging due to asymmetry among its
links. In this paper, we found that incorporating the direction of links
reveals new perspectives on communities regarding to two different roles,
source and terminal, that a node plays in each community. 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. The proposed algorithm shows great
performance and scalability on benchmark networks in simulations and
successfully recovers communities in real network applications.
