Directed graphs model asymmetric relationships between nodes and research on
directed graph embedding is of great significance in downstream graph analysis
and inference. Learning source and target embedding of nodes separately to
preserve edge asymmetry has become the dominant approach, but also poses
challenge for learning representations of low or even zero in/out degree nodes
that are ubiquitous in sparse graphs. In this paper, a collaborative
bi-directional aggregation method (COBA) for directed graphs embedding is
proposed by introducing spatial-based graph convolution. Firstly, the source
and target embeddings of the central node are learned by aggregating from the
counterparts of the source and target neighbors, respectively; Secondly, the
source/target embeddings of the zero in/out degree central nodes are enhanced
by aggregating the counterparts of opposite-directional neighbors (i.e.
target/source neighbors); Finally, source and target embeddings of the same
node are correlated to achieve collaborative aggregation. Extensive experiments
on real-world datasets demonstrate that the COBA comprehensively outperforms
state-of-the-art methods on multiple tasks and meanwhile validates the
effectiveness of proposed aggregation strategies