Social Network Attribute Analysis Method for Node Alignment Process - Mungen, Ahmet Anil; Geckil, Ayse; Kaya, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2019)
As users spend more time in social networks over the years, the number of social networks with different types of use is increasing day by day. Also, users are making more transactions and sharing more information day by day in social networks. Combining and sharing the information shared by users in different social networks into a single data space will increase the success of all data mining algorithms, especially community discovery. Our study proposes a new approach to identifying the same users, one of the most important parts of the process of identifying and associating individuals with accounts in different social networks. In this way, it has been put forward which features on social network basis stand out from other features and how they affect calculation success. F-Measure calculated how features affect success in identifying the same users and presented them on a social network basis. © 2019 IEEE.
1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 157111
Keywords: multiple social networks; node alignment; vector-based similarity algorithm