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Friend circle identification in ego network based on hybrid method

机译:基于杂交方法的自我网络中的朋友圈识别

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The ego network is a network of a user with his friends. The social network analysis method has provided some methods to help users classify their friends, including manually categorising friends or system classification. Whereas, categorising friends manually is time consuming. In this paper, we will discuss how to realise community identification automatically and accurately. To achieve this, we propose a method which utilises not only the similarity of user attributes but also the features of network structure and friends contact frequency. On the basis of the users' profile, we identify the relationship between them firstly. Second, we realise community identification using the structure features. Third, we introduce contact frequency to identify the relationship between users and their friends more accurately. Extensive experiments on real-world data show that our approach outperforms the state-of-the-art technique, in terms of balance error rate and F1 score.
机译:自我网络是与朋友的用户网络。社交网络分析方法提供了一些方法来帮助用户对其朋友进行分类,包括手动分类朋友或系统分类。而且,手动对朋友进行分类是耗时的。在本文中,我们将讨论如何自动准确地实现社区识别。为实现这一目标,我们提出了一种不仅利用用户属性的相似性,而且提出了一种方法,而且是网络结构和朋友接触频率的相似性。在用户的配置文件的基础上,我们首先识别它们之间的关系。其次,我们使用结构特征实现社区识别。第三,我们介绍了接触频率以更准确地识别用户与其朋友之间的关系。关于现实世界数据的广泛实验表明,我们的方法在平衡错误率和F1分数方面优于最先进的技术。

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