论文标题

部分可观测时空混沌系统的无模型预测

On the Persistence of Higher-Order Interactions in Real-World Hypergraphs

论文作者

Choo, Hyunjin, Shin, Kijung

论文摘要

超图是普通图的概括,它自然代表组相互作用为超预交(即节点的任意大小的子集)。这样的组互动在许多领域中无处不在:电子邮件的发件人和接收者,出版物的合着者以及客户共购买的项目,仅举几例。超图中的高阶相互作用(HOI)定义为在任何超边中的一组节点的共同表现。我们的重点是HOI的持久性随着时间的流逝而重复,这自然被解释为群体关系的优势,目的是回答三个问题:(a)现实世界中超图中的HOI如何随着时间的流逝而持续存在? (b)控制持久性的关键因素是什么? (c)我们如何准确预测持久性? 为了回答上面的问题,我们研究了来自6个领域的13个现实世界中的HOI的持久性。首先,我们定义了如何衡量HOI的持久性。然后,我们检查了持久性中的全球模式和异常情况,从而揭示了幂律关系。之后,我们研究了HOI的持久性与16个结构特征之间的关系,其中一些与持久性密切相关。最后,基于16个结构特征,我们评估了各种环境下持久性的可预测性,并找到了持久性的有力预测指标。请注意,预测HOIS的持久性具有许多潜在的应用程序,例如推荐要一起购买的物品并预测丢失的电子邮件收件人。

A hypergraph is a generalization of an ordinary graph, and it naturally represents group interactions as hyperedges (i.e., arbitrary-sized subsets of nodes). Such group interactions are ubiquitous in many domains: the sender and receivers of an email, the co-authors of a publication, and the items co-purchased by a customer, to name a few. A higher-order interaction (HOI) in a hypergraph is defined as the co-appearance of a set of nodes in any hyperedge. Our focus is the persistence of HOIs repeated over time, which is naturally interpreted as the strength of group relationships, aiming at answering three questions: (a) How do HOIs in real-world hypergraphs persist over time? (b) What are the key factors governing the persistence? (c) How accurately can we predict the persistence? In order to answer the questions above, we investigate the persistence of HOIs in 13 real-world hypergraphs from 6 domains. First, we define how to measure the persistence of HOIs. Then, we examine global patterns and anomalies in the persistence, revealing a power-law relationship. After that, we study the relations between the persistence and 16 structural features of HOIs, some of which are closely related to the persistence. Lastly, based on the 16 structural features, we assess the predictability of the persistence under various settings and find strong predictors of the persistence. Note that predicting the persistence of HOIs has many potential applications, such as recommending items to be purchased together and predicting missing recipients of emails.

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