Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio-temporal data from the real-time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio-temporal interaction data, focusing on two areas: exceptional subgroups and spatio-temporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.

中文翻译：

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“要来陪我玩吗？” 时空相互作用的离群子群发现

我们的生活是由社交互动组成的，社交互动可以通过个人小工具以及捕捉无处不在的社交数据的传感器进行记录。这种类型的数据，例如来自人们实时位置的时空数据，例如，可以用于推断可以转化为行为模式的交互。在本文中，我们考虑从时空交互数据中自动发现异常社会行为，重点关注两个领域：异常子群和时空异常值——均以描述模式的形式出现。为此，我们提出了一种发现特殊社会行为的方法，结合亚群发现和网络科学方法来识别偏离规范的行为。我们还建议使用两个异常值检测指标来识别异常值，即局部异常值因子 (LOF) 和 Voronoi 区域。我们将所提出的方法应用于合成数据以及两个包含在学校操场上玩耍的儿童的位置数据的真实数据集。我们的结果表明这是一种有效的方法，能够从数据中获取有意义的知识。