{"ID":2878843,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17236","arxiv_id":"2508.17236","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2508.17236","url_pdf":"https://arxiv.org/pdf/2508.17236v1","authors":"[\"Yunyong Ko\",\"Da Eun Lee\",\"Song Kyung Yu\",\"Sang-Wook Kim\"]","published":"2025-08-24T07:26:35Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.LG\"]","methods":"[]","has_code":false}