Issue No. 06 - June (2018 vol. 30)
Xiaoping Zhou , School of Information, Renmin University of China, Beijing, China
Xun Liang , School of Information, Renmin University of China, Beijing, China
Xiaoyong Du , School of Information, Renmin University of China, Beijing, China
Jichao Zhao , School of Information, Renmin University of China, Beijing, China
Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.
Social network services, Knowledge engineering, Reliability, Feature extraction, Electronic mail, Social computing
X. Zhou, X. Liang, X. Du and J. Zhao, "Structure Based User Identification across Social Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 6, pp. 1178-1191, 2018.