  Current directed graph embedding methods build upon undirected techniques but
often inadequately capture directed edge information, leading to challenges
such as: (1) Suboptimal representations for nodes with low in/out-degrees, due
to the insufficient neighbor interactions; (2) Limited inductive ability for
representing new nodes post-training; (3) Narrow generalizability, as training
is overly coupled with specific tasks. In response, we propose DUPLEX, an
inductive framework for complex embeddings of directed graphs. It (1) leverages
Hermitian adjacency matrix decomposition for comprehensive neighbor
integration, (2) employs a dual GAT encoder for directional neighbor modeling,
and (3) features two parameter-free decoders to decouple training from
particular tasks. DUPLEX outperforms state-of-the-art models, especially for
nodes with sparse connectivity, and demonstrates robust inductive capability
and adaptability across various tasks. The code is available at
https://github.com/alipay/DUPLEX.
