Abstract
The User Identity Resolution (UIR) problem is concerned with recognizing the same person with multiple virtual profiles in different online social networks (OSNs). Most of the existing methods focus only on the similarity of profile attributes or simply combine the profile attributes and linkages of friends. In this paper, we propose a novel Greedy-based Cross-Matching (GCM) algorithm, which combines profile attributes with linkage information of both friend and non-friend users. In the GCM algorithm, we first propose a greedy strategy for detecting candidate matching users using Profile Attributes Similarity (PAS) and User Surrounding Score (USS). We then define the User Matching Score (UMS), which combines PAS with network structures, to greedily determine matched users for the candidate ones. Finally, we utilize a novel cross-matching process inspired by Stable Marriage Problem (SMP) to further improve the matching accuracy. Experiments on Twitter and Facebook demonstrate that our method significantly improves the matching performance and outperforms the state-of-the-art algorithms.
This work was supported by National Science Foundation of China (No. 61272374,61300190), Specialized Research Fund for the Doctoral Program of Higher Education (No.20120041110046) and Key Project of Chinese Ministry of Education (No. 313011).
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Liang, W., Meng, B., He, X., Zhang, X. (2015). GCM: A Greedy-Based Cross-Matching Algorithm for Identifying Users Across Multiple Online Social Networks. In: Chau, M., Wang, G., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2015. Lecture Notes in Computer Science(), vol 9074. Springer, Cham. https://doi.org/10.1007/978-3-319-18455-5_4
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DOI: https://doi.org/10.1007/978-3-319-18455-5_4
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