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arXiv · 2508.17236

Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks

Yunyong Ko·Da Eun Lee·Song Kyung Yu·Sang-Wook Kim
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Abstract

Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for Learning hIgh-order dyNamiCs Of reaL-world Networks, that employs (1) bi-interactional hyperedge encoding for short-term patterns, (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.

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Yunyong Ko, Da Eun Lee, Song Kyung Yu, Sang-Wook Kim. 2025-08-24. Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks. https://doi.org/10.1145/3746252.3760902

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