时序交互数据中动态超图表示的推断
Inference of dynamic hypergraph representations in temporal interaction data
AI总结：
针对时序交互数据中动态超图表示的快照选择问题，提出基于最小描述长度原理的非参数方法，能提取最优快照，在噪声下恢复结构并揭示活动波动。
摘要：
社会和自然科学中的一系列系统会生成由两个不同类别项目在不同时间实例上的交互所组成的数据集。例如，在线购物会生成形式为（用户，产品，购买时间）的购买事件，而植物-传粉者系统中的互利共生交互会生成形式为（昆虫，植物，传粉时间）的传粉事件。这些数据集可以被有意义地建模为时序超图快照，其中某一类别中的多个项目（如在线购物者）如果在给定时间窗口内与另一类别中的共同项目（如购买了同一产品）发生过交互，则共享一条超边，从而允许应用超图分析技术。然而，通常不清楚如何选择这些时序快照的数量和持续时间，而这些选择对最终的超图表示有着强烈的影响。在此，我们提出了一种基于原则的非参数解决方案，即根据最小描述长度原理，提取能够最优捕获时序事件数据中结构规律性的时序超图快照。我们在真实和合成数据集上演示了我们的方法，发现该方法能够在存在显著噪声的情况下恢复植入的人工超图结构，并揭示人类移动数据中有意义的活动波动。
英文摘要：
A range of systems across the social and natural sciences generate datasets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.e. online shoppers) share a hyperedge if they interacted with a common item in the other category (i.e. purchased the same product) within a given time window, allowing for the application of hypergraph analysis techniques. However, it is often unclear how to choose the number and duration of these temporal snapshots, which have a strong influence on the final hypergraph representations. Here we propose a principled nonparametric solution to this problem by extracting temporal hypergraph snapshots that optimally capture structural regularities in temporal event data according to the minimum description length principle. We demonstrate our methods on real and synthetic datasets, finding that they can recover planted artificial hypergraph structure in the presence of considerable noise and reveal meaningful activity fluctuations in human mobility data.
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