利用超图识别并表征移动网络中的高阶交互
Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs
AI总结：
针对传统移动模型仅捕捉地点成对交互、忽略高阶关系的问题，提出基于时间窗口和频繁模式挖掘的共访问超图模型，经公开数据集验证，可有效分析城市移动模式等，在多领域有应用潜力。
摘要：
理解人类移动对从城市规划到公共卫生的各类应用至关重要。传统移动模型（如流网络、共位矩阵）仅捕捉离散地点间的成对交互，忽略了地点间的高阶关系（即两个及以上地点间的移动流）。为解决该问题，我们提出共访问超图（co-visitation hypergraphs）模型，该模型利用时间观测窗口从个体移动轨迹数据中提取地点间的群体交互。通过频繁模式挖掘，我们的方法构建的超图可捕捉不同时空尺度下的动态移动行为。我们在一个公开移动数据集上验证了方法，并证明其在分析城市尺度移动模式、检测极端天气事件等外部干扰期间的变化、研究地点的连接性（度）与其内部兴趣点（POIs）数量的关联方面的有效性。结果表明，我们基于超图的移动分析框架是一种有价值的工具，在公共卫生、灾害韧性、城市规划等多元领域具有应用潜力。
英文摘要：
Understanding human mobility is essential for applications ranging from urban planning to public health. Traditional mobility models such as flow networks and colocation matrices capture only pairwise interactions between discrete locations, overlooking higher-order relationships among locations (i.e., mobility flow among two or more locations). To address this, we propose co-visitation hypergraphs, a model that leverages temporal observation windows to extract group interactions between locations from individual mobility trajectory data. Using frequent pattern mining, our approach constructs hypergraphs that capture dynamic mobility behaviors across different spatial and temporal scales. We validate our method on a publicly available mobility dataset and demonstrate its effectiveness in analyzing city-scale mobility patterns, detecting shifts during external disruptions such as extreme weather events, and examining how a location's connectivity (degree) relates to the number of points of interest (POIs) within it. Our results demonstrate that our hypergraph-based mobility analysis framework is a valuable tool with potential applications in diverse fields such as public health, disaster resilience, and urban planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
