Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks
This work addresses the challenge of predicting dynamic high-order relationships in networks, which is important for applications like social or biological network analysis, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of modeling high-order dynamics in real-world networks by capturing short-term structural influences and long-term periodic reappearances, and shows that their method LINCOLN outperforms nine state-of-the-art methods in dynamic hyperedge prediction.
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.