Müngen, A.A.Geckil, A.Kaya, M.2026-03-262026-03-262019978172813992010.1109/UBMYK48245.2019.89656162-s2.0-85079226586https://doi.org/10.1109/UBMYK48245.2019.8965616https://hdl.handle.net/20.500.14901/3833As 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.eninfo:eu-repo/semantics/closedAccessMultiple Social NetworksNode AlignmentVector-Based Similarity AlgorithmConference Object