Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
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.
Keywords
Cite
@article{arxiv.2508.17236,
title = {Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks},
author = {Yunyong Ko and Da Eun Lee and Song Kyung Yu and Sang-Wook Kim},
journal= {arXiv preprint arXiv:2508.17236},
year = {2025}
}
Comments
5 pages, 4 figures, 2 tables, ACM International Conference on Information and Knowledge Management (CIKM) 2025