Data mining methods focus on discovering models and patterns from large databases that summarize the data. However, generating such results is not an end in itself because their applicability is not straightforward. Ideally, the user would ultimately like to use them to decide what actions to take. Action mining explicitly emerged as a response to this need. Currently, most of the action mining methods rely on simple data which describes each object independently that means they do not take into account relationships between objects. In social networks, relationships enable an individual to influence another one, so ignoring them in action mining process would lead to miss some profitable actions. In this paper, we introduce action mining from social networks. In fact, our main contribution is to extract cost-effective actions which is formulated as an optimization problem where the objective is to learn actions consisting of the changes in the network that are likely to result in desired changes in the labels of intended individuals while minimizing the cost of the changes. Experiments confirm that the proposed approach performs much better than the current state-of-the-art in action mining.

中文翻译：

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从社交网络中提取动作

数据挖掘方法侧重于从汇总数据的大型数据库中发现模型和模式。然而，产生这样的结果本身并不是目的，因为它们的适用性并不简单。理想情况下，用户最终希望使用它们来决定采取什么行动。动作挖掘明确地作为对这种需求的回应而出现。目前，大多数动作挖掘方法依赖于独立描述每个对象的简单数据，这意味着它们没有考虑对象之间的关系。在社交网络中，关系使一个人能够影响另一个人，因此在动作挖掘过程中忽略它们会导致错过一些有利可图的动作。在本文中，我们介绍了来自社交网络的动作挖掘。实际上，我们的主要贡献是提取具有成本效益的行动，该行动被表述为一个优化问题，其目标是学习由网络变化组成的行动，这些变化可能导致预期个体的标签发生所需的变化，同时最小化成本变化。实验证实，所提出的方法在动作挖掘方面的表现比当前最先进的方法要好得多。