Authors: Fan Xing; Tinghuai Ma; Meili Tang; Donghai Guan
Addresses: School of Computer and Software, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210044, China ' Jiangsu Engineering Centre of Network Monitoring, CICAEET, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210044, China ' School of Public Administration, Nanjing University of Information Science and Technology, Jiangsu, Nanjing, 210044, China ' School of Computer, Nanjing University of Aeronautics and Astronautics, Jiangsu, Nanjing, 210016, China
Abstract: 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.
Keywords: ego networks; friend circle; communities; user attribute; network structure; contact frequency.
International Journal of Ad Hoc and Ubiquitous Computing, 2019 Vol.30 No.4, pp.224 - 234
Available online: 31 Mar 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article