D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
This work solves the problem of improving representation learning for directed graphs, which is crucial for tasks like node classification and link prediction, but it appears incremental as it builds on existing graph neural network approaches.
The paper tackles the problem of learning representations for directed graphs (digraphs) by addressing limitations in prior methods, such as poor generalizability and neglect of neighborhood modeling and asymmetry preservation, and introduces D-HYPR, which statistically significantly outperforms state-of-the-art techniques across 8 real-world datasets.
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