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.

翻译：当前的有向图嵌入方法多基于无向图技术，但往往未能充分捕捉有向边信息，导致以下挑战：(1) 由于邻居交互不足，入度/出度较低节点的表示效果欠佳；(2) 对训练后新节点的表示归纳能力有限；(3) 泛化性较弱，训练过程与特定任务过度耦合。为此，我们提出DUPLEX——一种面向有向图复数嵌入的归纳式框架。该框架(1)利用埃尔米特邻接矩阵分解实现全面的邻居整合，(2)采用双重GAT编码器进行方向性邻居建模，(3)配备两个无参数解码器以解耦训练与特定任务的关联。DUPLEX在多个基准测试中优于现有最优模型，尤其在连接稀疏节点上表现突出，并展现出强大的归纳能力与跨任务适应性。代码已开源：https://github.com/alipay/DUPLEX。