Investigating Neighborhood Modeling and Asymmetry Preservation in Digraph Representation Learning
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Graph Neural Networks (GNNs) traditionally exhibit poor performance for directed graphs (digraphs) due to notable challenges in 1) modeling neighborhoods and 2) preserving asymmetry. In this paper, we address these challenges in traditional GNNs by leveraging hyperbolic collaborative learning from multi-ordered and partitioned neighborhoods, and regularizers inspired by socio-psychological factors. Our resulting formalism, Digraph Hyperbolic Network (D-HYPR) learns node representations in hyperbolic space to avoid structural and semantic distortion of real-world digraphs. We conduct comprehensive experimentation on 4 tasks: link prediction, node classification, sign prediction, and embedding visualization. D-HYPR statistically significantly outperforms the current state of the art on a majority of tasks and datasets, while achieving competitive performance otherwise. Our code and data will be available.
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