Online social networks (OSNs) have become one of the most popular platforms where people communicate by sharing contents and personal information. The interactions performed by the users allow to identify the homophily between users and reveal the presence of several communities that could depend on several factors: such as the type of relationships (eg, colleagues and school mates) or to users' preferences (eg, users' interests or hobbies). A very important issue in this scenario is the necessary to characterize such communities by using known real properties or attributes about their members. In this article, we propose an approach that identifies the communities of users by exploiting several community detection algorithms. Afterward, for each user, we exploit decision trees to find a model that describes and distinguishes community affiliations based on known attributes of the members. The evaluation of our approach is derived from a real dataset which consists of the profile information, relationships, and interactions of 95 716 Facebook users. The experimental results show that the proposed approach is able to correctly recognize which attributes of the members properly characterize their corresponding community while ensuring a high level of accuracy (about 85%).

中文翻译：

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利用同质性来表征在线社交网络中的社区

在线社交网络（OSN）已成为人们通过共享内容和个人信息进行交流的最受欢迎的平台之一。用户执行的交互可以识别用户之间的同质性，并揭示可能取决于几个因素的多个社区的存在：例如关系的类型（例如，同事和同学）或用户的偏好（例如，用户）的兴趣或爱好）。在这种情况下，一个非常重要的问题是必须通过使用有关其成员的已知真实属性或属性来表征此类社区。在本文中，我们提出了一种通过利用几种社区检测算法来识别用户社区的方法。之后，对于每个用户，我们利用决策树找到一个模型，该模型根据成员的已知属性来描述和区分社区隶属关系。我们的方法的评估来自一个真实的数据集，该数据集包含95 716个Facebook用户的个人资料信息，关系和交互。实验结果表明，所提出的方法能够正确识别成员的哪些属性正确地表征了其相应的社区，同时确保了较高的准确性（大约85％）。