Event analysis from news and social networks is a promising way to understand complex social phenomena. Each event consists of different components, which indicate what happened, when, where, and the people and organizations involved. Heterogeneous networks are useful for modeling large event datasets, where we map different types of objects (e.g. events and their components), as well as the different relationships between objects. Such networks enable the identification of related events, in which users label some events in categories and then use the network’s topological structure to find other events of interest. Although this process can be automated, there is a lack of machine learning methods to properly handle event classification from heterogeneous networks. In this paper, we present the framework named Heterogeneous Event Network Regularization in Two-stages (HENR²). The first stage of HENR² aims to learn the importance level of each relationship between events and their components. In the second stage, the regularization process considers the importance levels of each relationship to propagate labels on the network. Thus, the classification process is improved by considering the domain characteristics of the event dataset, such as temporal seasonality and geographical distribution. In both stages, our approach also deals with noisy data through parameters that define the confidence level of labeled events during label propagation. Experimental results involving twelve event networks from different application domains show that our proposal outperforms existing regularization frameworks.

新闻和社交网络的事件分析是了解复杂社会现象的一种有前途的方式。每个事件由不同的组件组成，这些组件指示发生了什么事情，发生的时间，地点以及所涉及的人员和组织。异构网络对于大型事件数据集的建模非常有用，我们可以在其中映射不同类型的对象（例如，事件及其组件）以及对象之间的不同关系。这样的网络使相关事件的识别成为可能，其中用户在类别中标记一些事件，然后使用网络的拓扑结构查找其他感兴趣的事件。尽管此过程可以自动化，但是仍然缺少机器学习方法来正确处理来自异构网络的事件分类。在本文中，²）。HENR ²的第一阶段旨在了解事件及其组成部分之间每个关系的重要性级别。在第二阶段，正则化过程将考虑每个关系的重要性级别，以在网络上传播标签。因此，通过考虑事件数据集的域特征（例如时间季节性和地理分布）来改进分类过程。在两个阶段中，我们的方法还通过定义标签传播过程中标记事件的置信度的参数来处理嘈杂的数据。涉及来自不同应用程序域的十二个事件网络的实验结果表明，我们的建议优于现有的正则化框架。