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Friends Classification of Ego Network Based on Combined Features

Jing Jiaa, Tinghuai Mab, Fan Xinga, William Faraha, Donghai Guana,c

a School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, China;
b CICAEET, Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, China;
c School of Computer, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu, China

* Corresponding Author: Tinghuai Ma, email

Intelligent Automation & Soft Computing 2018, 24(4), 819-827. https://doi.org/10.1080/10798587.2017.1355656

Abstract

Ego networks consist of a user and his/her friends and depending on the number of friends a user has, makes them cumbersome to deal with. Social Networks allow users to manually categorize their “circle of friends”, but in today’s social networks due to the unlimited number of friends a user has, it is imperative to find a suitable method to automatically administrate these friends. Manually categorizing friends means that the user has to regularly check and update his circle of friends whenever the friends list grows. This may be time consuming for users and the results may not be accurate enough. In this paper, to solve this problem, we present a method, which combining user attributes, network structure and contact frequent three aspects. Efficiently using the profile of users, we first identify the relationship between them and then we attempt to solve the problem of community identification when a user’s profile is missing or inaccessible by use of ego network structural features. Lastly, to obtain more accurate results and realize updates automatically, we attempt to find those friends who have frequent contacts with the user. We compare the performance of the proposed algorithm with other methods, and the results show that our method has significant advantages to them.

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Cite This Article

. Jing Jia, . Tinghuai Ma, . Fan Xing, . William Farah and . Donghai Guan, "Friends classification of ego network based on combined features," Intelligent Automation & Soft Computing, vol. 24, no.4, pp. 819–827, 2018.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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