Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable knowledge in each specific domain. An action is a new tool in this research area that suggests some changes to the user to gain a profit in his/her domain. Currently, most of action mining methods rely on simple data which describes each object independently. Since social data has more complex structure due to the relationships between individuals, a major problem is that such structural information is not taken into account in the action mining process. This leads to miss some useful knowledge and profitable actions. Consequently, more effective methods are needed for mining actions.

The main focus of this work is to extract cost-effective actions from social networks in which nodes have attributes. The actions suggest optimal changes in nodes’ attributes that are likely to result in changing labels of users to more desired one when they are applied. We develop an action mining method based on Random Walks that naturally combines the information from the network structure with nodes attributes. We formulate action mining as an optimization problem where the goal is to learn a function that varies the values of nodes’ attributes which in turn affect edges’ weights in the network so that the labels of intended individuals are likely to take the desired label while minimizing the cost of incurring the changes. Experiments confirm that the proposed approach outperforms the current state-of-the-art in action mining.

可行的知识发现最近吸引了很多兴趣。这几乎是向每个特定领域中挖掘更多可用和更适用知识的新范式转变。动作是该研究领域中的一种新工具，它建议用户进行一些更改以在其领域中获利。当前，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。由于社交数据由于个人之间的关系而具有更复杂的结构，因此一个主要问题是在动作挖掘过程中未考虑此类结构信息。这导致错过一些有用的知识和有益的行动。因此，需要更有效的挖掘行动方法。

这项工作的主要重点是从节点具有属性的社交网络中提取具有成本效益的行动。这些操作建议对节点属性进行最佳更改，这可能会导致在应用用户时将用户标签更改为更期望的标签。我们开发了一种基于随机游动的动作挖掘方法，该方法自然地将来自网络结构的信息与节点属性相结合。我们将动作挖掘公式化为一个优化问题，其目标是学习一种功能，该功能会改变节点属性的值，从而影响网络中边缘的权重，以便目标个体的标签在最小化的情况下很可能采用所需的标签进行更改的成本。实验证实，所提出的方法优于当前的动作挖掘技术。