Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs). Prior work in DRL is largely constrained (e.g., limited to directed acyclic graphs), or has poor generalizability across tasks (e.g., evaluated solely on one task). Most Graph Neural Networks (GNNs) exhibit poor performance on digraphs due to the neglect of modeling neighborhoods and preserving asymmetry. In this paper, we address these notable challenges by leveraging hyperbolic collaborative learning from multi-ordered and partitioned neighborhoods, and regularizers inspired by socio-psychological factors. Our resulting formalism, Digraph Hyperbolic Networks (D-HYPR) - albeit conceptually simple - generalizes to digraphs where cycles and non-transitive relations are common, and is applicable to multiple downstream tasks including node classification, link presence prediction, and link property prediction. In order to assess the effectiveness of D-HYPR, extensive evaluations were performed across 8 real-world digraph datasets involving 21 prior techniques. D-HYPR statistically significantly outperforms the current state of the art. We release our code at https://github.com/hongluzhou/dhypr

翻译：DRL的先前工作受到很大限制(例如,限于定向单环图),或任务之间不太普遍(例如,只对一项任务进行评价),大多数图形神经网络(GNN)由于忽略了建模区和保持不对称性,在地谱上表现不佳。在本文件中,我们通过利用多顺序和分隔的邻里以及受社会心理因素启发的正规化者的双向合作学习来应对这些显著的挑战。我们由此产生的形式主义、《海拔高偏向网络》(D-HYPR)――尽管在概念上简单,但一般化为循环和非透明关系常见的分划线,并适用于多个下游任务,包括节点分类、存在预测和地产预测。为了评估D-HYPR的有效性,在涉及21种先前技术的8个现实世界分层数据集中进行了广泛的评估。D-HYPR在统计上大大超越了我们目前状态的艺术代码。