  Node representation learning for directed graphs is critically important to
facilitate many graph mining tasks. To capture the directed edges between
nodes, existing methods mostly learn two embedding vectors for each node,
source vector and target vector. However, these methods learn the source and
target vectors separately. For the node with very low indegree or outdegree,
the corresponding target vector or source vector cannot be effectively learned.
In this paper, we propose a novel Directed Graph embedding framework based on
Generative Adversarial Network, called DGGAN. The main idea is to use
adversarial mechanisms to deploy a discriminator and two generators that
jointly learn each node's source and target vectors. For a given node, the two
generators are trained to generate its fake target and source neighbor nodes
from the same underlying distribution, and the discriminator aims to
distinguish whether a neighbor node is real or fake. The two generators are
formulated into a unified framework and could mutually reinforce each other to
learn more robust source and target vectors. Extensive experiments show that
DGGAN consistently and significantly outperforms existing state-of-the-art
methods across multiple graph mining tasks on directed graphs.
