Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914002120998912 |
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| author | Ko, Yunyong Lee, Da Eun Yu, Song Kyung Kim, Sang-Wook |
| author_facet | Ko, Yunyong Lee, Da Eun Yu, Song Kyung Kim, Sang-Wook |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17236 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks Ko, Yunyong Lee, Da Eun Yu, Song Kyung Kim, Sang-Wook Social and Information Networks Machine Learning 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. |
| title | Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2508.17236 |