The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.

如今，在线社交网络（OSN）的大量使用使用户暴露了更多的信息而没有意识到。恶意用户或营销机构现在可以通过使用目标朋友的数据来推断未在OSN上发布的信息，以使他们受益。在本文中，作者提出了一种可概括的方法，该方法能够利用他们的Twitter关系和组织中图表的特征来推断组织的角色。作者还对节点中心点进行了广泛的分析，以研究它们在所建议的不同类的推论中的作用。从对中心进行的实验和消融研究得出，作者得出的结论是，在对组织员工的角色进行分类时，图形的潜在特征以及有向关系比以前提出的方法表现更好。此外，为了评估该方法，作者还贡献了一个新的数据集，该数据集由三个有向图（每个组织一个）组成，这些图表示从Twitter获得的员工之间的关系。