Describing group evolution in temporal data using multi-faceted events

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Pub Date : 2024-08-01 DOI:10.1007/s10994-024-06600-4
Andrea Failla, Rémy Cazabet, Giulio Rossetti, Salvatore Citraro
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Abstract

Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.

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利用多方面事件描述时间数据中的群体演变
在处理各种数据挖掘任务时,群体(如点群或节点群)是最基本的。在时态数据中,描述群体演变的主要方法是识别 "事件"。然而,文献中通常描述的事件,如收缩/增长、分裂/合并,往往是任意定义的,这就在此类理论/预定义类型与实际数据群体观察之间造成了差距。超越现有的分类法,我们将事件视为 "原型",其特点是独特的量化维度组合,我们称之为 "面"。群体动态由其在 "面 "空间中的位置来定义,原型事件在 "面 "空间中占据极端位置。因此,我们的方法并不强制要求严格的事件类型,而是允许对涉及群体接近多种原型的动态进行混合描述。我们将我们的框架应用于几个面对面互动数据集中不断演化的群体,结果表明,与最先进的方法相比,它能对群体动态进行更丰富、更可靠的描述,尤其是在群体关系复杂的情况下。我们的方法还为与动态群体分析相关的常见任务提供了直观的解决方案,例如选择合适的聚合规模、量化分区稳定性和评估事件质量。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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