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    <abstract>Graph representation learning (GRL) in hyperbolic space has gradually emerged as a promising approach. Meanwhile, masking and reconstruction-based (MR-based) methods lead to state-of-the-art self-supervised graph representation. However, existing MR-based methods do not fully consider deep node and structural information. Inspired by the recent active and emerging field of self-supervised learning, we propose a novel node and edge dual-masked self-supervised graph representation learning framework in hyperbolic space, named HDM-GAE. We have designed a graph dual-masked module and a hyperbolic structural self-attention encoder module to mask nodes or edges and perform node aggregation within hyperbolic space, respectively. Comprehensive experiments and ablation studies on real-world multi-category datasets, demonstrate the superiority of our method in downstream tasks such as node classification and link prediction.</abstract>
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%0 Conference Proceedings
%T Graph Representation Learning in Hyperbolic Space via Dual-Masked
%A Gong, Rui
%A Jiang, Zuyun
%A Zha, Daren
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F gong-etal-2025-graph
%X Graph representation learning (GRL) in hyperbolic space has gradually emerged as a promising approach. Meanwhile, masking and reconstruction-based (MR-based) methods lead to state-of-the-art self-supervised graph representation. However, existing MR-based methods do not fully consider deep node and structural information. Inspired by the recent active and emerging field of self-supervised learning, we propose a novel node and edge dual-masked self-supervised graph representation learning framework in hyperbolic space, named HDM-GAE. We have designed a graph dual-masked module and a hyperbolic structural self-attention encoder module to mask nodes or edges and perform node aggregation within hyperbolic space, respectively. Comprehensive experiments and ablation studies on real-world multi-category datasets, demonstrate the superiority of our method in downstream tasks such as node classification and link prediction.
%U https://aclanthology.org/2025.coling-main.43/
%P 637-646

Markdown (Informal)

[Graph Representation Learning in Hyperbolic Space via Dual-Masked](https://aclanthology.org/2025.coling-main.43/) (Gong et al., COLING 2025)

• Graph Representation Learning in Hyperbolic Space via Dual-Masked (Gong et al., COLING 2025)

ACL

• Rui Gong, Zuyun Jiang, and Daren Zha. 2025. Graph Representation Learning in Hyperbolic Space via Dual-Masked. In Proceedings of the 31st International Conference on Computational Linguistics, pages 637–646, Abu Dhabi, UAE. Association for Computational Linguistics.