We are faced with data comprised of entities interacting over time: this can be individuals meeting, customers buying products, machines exchanging packets on the IP network, among others. Capturing the dynamics as well as the structure of these interactions is of crucial importance for analysis. These interactions can almost always be labeled with content: group belonging, reviews of products, abstracts, etc. We model these stream of interactions as stream graphs, a recent framework to model interactions over time. Formal Concept Analysis provides a framework for analyzing concepts evolving within a context. Considering graphs as the context, it has recently been applied to perform closed pattern mining on social graphs. In this paper, we are interested in pattern mining in sequences of interactions. After recalling and extending notions from formal concept analysis on graphs to stream graphs, we introduce algorithms to enumerate closed patterns on a labeled stream graph, and introduce a way to select relevant closed patterns. We run experiments on two real-world datasets of interactions among students and citations between authors, and show both the feasibility and the relevance of our method.

中文翻译：

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探索和挖掘归因的相互作用序列

我们面临着由随时间交互的实体组成的数据：这可以是个人会议、客户购买产品、在 IP 网络上交换数据包的机器等。捕捉动态以及这些相互作用的结构对于分析至关重要。这些交互几乎总是可以用内容来标记：群体归属、产品评论、摘要等。我们将这些交互流建模为流图，这是一种随着时间的推移对交互进行建模的最新框架。形式概念分析提供了一个框架，用于分析在上下文中演变的概念。将图视为上下文，它最近已被应用于对社交图执行封闭模式挖掘。在本文中，我们对交互序列中的模式挖掘感兴趣。在回忆和扩展从图的形式概念分析到流图的概念之后，我们引入算法来枚举标记流图上的闭合模式，并介绍一种选择相关闭合模式的方法。我们对学生之间的交互和作者之间的引用的两个真实世界数据集进行了实验，并展示了我们方法的可行性和相关性。