고윤용/이다은/유송경’s paper has been accepted in

Title: Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
Author: Yunyong Ko, DaEun Lee, Songkyung Yu, and Sang-Wook Kim
Abstract
Real-world networks have high-order relationships among objects and they evolve over time. To capture such crucial properties, dynamic hypergraph learning has 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 and (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN consistently outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.