发表机构
Central European University; Luiss University（中欧大学; 鲁伊斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出时间超图模体概念及精确枚举算法，通过动态规划加速并引入零模型，在多个真实数据集上揭示系统局部组织模式。
AI 中文摘要
网络模体，即图中反复出现的局部交互模式，为理解复杂系统中结构与功能之间的相互作用提供了基本见解。许多现实世界系统并不能被传统的静态成对网络很好地表示，因为交互可能涉及节点组、随时间发生或编码方向性。在本文中，我们引入了超图和有向超图的时间模体，将模体分析扩展到带时间戳的多体交互。我们形式化了相应的挖掘问题，研究了这些模体的组合结构，并开发了用于枚举它们的精确算法。特别地，我们提出了一种动态规划算法，该算法大幅降低了模体挖掘的计算成本，在经验数据集上实现了数量级的加速。我们还为时间超图引入了一个零模型，以评估模体的统计过表达和欠表达。将该框架应用于来自不同领域的真实世界数据集，包括面对面接触、科学合作、电子邮件交流和比特币交易，我们表明时间超图模体揭示了跨系统的不同局部组织形式。最后，我们通过聚焦于科学合作和电子邮件通信中持久模式的案例研究，展示了它们作为探索性工具的用途。
英文摘要
Network motifs, recurrent local patterns of interactions in graphs, provide fundamental insights on the interplay between structure and functionality in complex systems. Many real-world systems are not well represented by traditional static pairwise networks, as interactions may involve groups of nodes, occur over time, or encode directionality. In this paper, we introduce temporal motifs for hypergraphs and directed hypergraphs, extending motif analysis to timestamped many-body interactions. We formalize the corresponding mining problem, study the combinatorial structure of these motifs, and develop exact algorithms for their enumeration. In particular, we propose a dynamic programming algorithm that substantially reduces the computational cost of motif mining, achieving orders of magnitude speedups on empirical datasets. We also introduce a null model for temporal hypergraphs to assess the statistical over- and under-expression of motifs. Applying the proposed framework to real-world datasets from different domains, including face-to-face contacts, scientific collaborations, e-mail exchanges, and Bitcoin transactions, we show that temporal hypergraph motifs reveal distinct forms of local organization across systems. Finally, we demonstrate their use as an exploratory tool through focused case studies on persistent patterns in scientific collaborations and e-mail communications.
