标题： 记忆动力学的简洁网络模型揭示路径数据中的可解释模式 显示英文标题

标题： Concise network models of memory dynamics reveal explainable patterns in path data

摘要： 网络是建模跨尺度复杂系统结构和动态的强大工具。 系统组件之间的直接连接通常表示为边，而路径和行走则捕捉间接相互作用。 这种方法假设系统中的流动是独立转换的序列。 现实世界系统中的路径数据通常具有高阶依赖性，这需要更复杂的模型。 在本工作中，我们提出了一种从路径数据构建简洁网络的方法，该方法在一阶和二阶模型之间进行插值。 我们通过创建捕获二阶效应潜在模式的状态节点，并引入一个可解释的度量来平衡模型大小和准确性，优先考虑简单性和可解释性。 在合成和现实世界的应用中，我们的方法揭示了大规模的记忆模式，并构建了简洁的网络，其提供的见解超越了一阶模型，而其规模仅为二阶模型的一小部分。 显示英文摘要

摘要： Networks are a powerful tool to model the structure and dynamics of complex systems across scales. Direct connections between system components are often represented as edges, while paths and walks capture indirect interactions. This approach assumes that flows in the system are sequences of independent transitions. Path data from real-world systems often have higher-order dependencies, which require more sophisticated models. In this work, we propose a method to construct concise networks from path data that interpolate between first and second-order models. We prioritise simplicity and interpretability by creating state nodes that capture latent modes of second-order effects and introducing an interpretable measure to balance model size and accuracy. In both synthetic and real-world applications, our method reveals large-scale memory patterns and constructs concise networks that provide insights beyond the first-order model at the fraction of the size of a second-order model.