The aim of the paper is to identify and categorize frequent patterns describing interactions between users in social networks. We analyze a social network with relationships between users that evolve in time already identified. In our research, we discover patterns based on frequent interactions between groups of users. The patterns are described by the characteristics of these interactions, such as their reciprocity, or the relative difference between estimations of global influences of the users participating in the discussions. The modification of the apriori algorithms is applied as one of the methods for pattern identification. The analyzed social network is built using the data set containing data from the Polish blog website salon24, which concerns mostly socio-political issues.

中文翻译：

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博客圈中频繁的用户交互模式

本文的目的是对描述社交网络中用户之间交互的频繁模式进行识别和分类。我们分析了一个社交网络，其中用户之间的关系随着时间的推移已经确定。在我们的研究中，我们发现了基于用户组之间频繁交互的模式。这些交互的特征（例如，交互性或参与讨论的用户的总体影响估计之间的相对差异）描述了模式。先验算法的修改被用作模式识别的方法之一。使用包含来自波兰博客网站salon24的数据的数据集构建分析后的社交网络，该数据集主要涉及社会政治问题。