Abstract
Abundance of online social platforms allows users to create more than one profile in different social networks. Several issues arise due to multiple user identities including data integration and information retrieval in social networks. Identifying user profiles across social platforms is known as entity resolution. In this paper, we propose a degree-based method to attack entity resolution problems. More precisely, we utilize the degree of users and their friends to identify user profiles. Our results show that, without help of critical information such as e-mail addresses, the proposed method can outperform existing string matching-based solutions as well as popular classifiers such as SVM and Naive Bayes.
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Abdallah A, Maarof MA, Zainal A (2016) Fraud detection system: a survey. J Netw Comput Appl 68(Supplement C):90–113. https://doi.org/10.1016/j.jnca.2016.04.007. http://www.sciencedirect.com/science/article/pii/S1084804516300571
Adewole KS, Anuar NB, Kamsin A, Varathan KD, Razak SA (2017) Malicious accounts: dark of the social networks. J Netw Comput Appl 79(Supplement C):41–67. https://doi.org/10.1016/j.jnca.2016.11.030. http://www.sciencedirect.com/science/article/pii/S1084804516303009
Alvarez JJ, Mendoza FA, Labrador M (2017) An accurate way to cross reference users across social networks. In: SoutheastCon 2017, pp 1–6. https://doi.org/10.1109/SECON.2017.7925366
Barta G (2014) A link-based approach to entity resolution in social networks. CoRR. arXiv:1404.3017
Bhattacharya I, Getoor L (2007) Collective entity resolution in relational data. ACM Trans Knowl Discov Data 1(1):5. https://doi.org/10.1145/1217299.1217304
Bilgic M, Licamele L, Getoor L, Shneiderman B (2006) D-dupe: an interactive tool for entity resolution in social networks. Springer, Berlin, pp 505–507. https://doi.org/10.1007/11618058_46
Brizan DG, Tansel AU (2006) A survey of entity resolution and record linkage methodologies. Commun IIMA 6(3):41–50. http://www.iima.org/CIIMA/8%20CIIMA%206-3%2041-50%20%20Brizan.pdf
Campbell WM, Li L, Dagli CK, Acevedo-Aviles J, Geyer K, Campbell JP, Priebe C (2016) Cross-domain entity resolution in social media. CoRR. arXiv: abs/1608.01386. http://arxiv.org/abs/1608.01386
Domingos P, Lowd D, Kok S, Nath A, Poon H, Richardson M, Singla P (2010) Markov logic: a language and algorithms for link mining. Springer, New York, pp 135–161. https://doi.org/10.1007/978-1-4419-6515-8_5
Getoor L, Machanavajjhala A (2012) Entity resolution: theory, practice and open challenges. Proc VLDB Endow 5(12):2018–2019. https://doi.org/10.14778/2367502.2367564
Getoor L, Machanavajjhala A (2013) Entity resolution for big data. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13, ACM, New York, NY, USA, p 1527. https://doi.org/10.1145/2487575.2506179
Lee J, Oh JC (2014) Estimating the degrees of neighboring nodes in online social networks. In: Dam HK, Pitt J, Xu Y, Governatori G, Ito T (eds) PRIMA 2014: principles and practice of multi-agent systems. Springer International Publishing, Cham, pp 42–56
Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10(8):707–710
Malhotra A, Totti L, Meira Jr. W, Kumaraguru P, Almeida V (2012) Studying user footprints in different online social networks. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012), ASONAM ’12, IEEE Computer Society, Washington, DC, USA, pp 1065–1070. https://doi.org/10.1109/ASONAM.2012.184
Opitz B, Sztyler T, Jess M, Knip F, Bikar C, Pfister B, Scherp A (2014) An approach for incremental entity resolution at the example of social media data. In: Stefanidakis M, Fabbrizio GD, Papadakis I (eds) Proceedings of the AI Mashup challenge 2014 co-located with 11th extended semantic web conference (ESWC 2014), Crete, Greece, 27 May 2014, vol 1200 of CEUR workshop proceedings, CEUR-WS.org. http://ceur-ws.org/Vol-1200/paper1.pdf
Peled O, Fire M, Rokach L, Elovici Y (2013) Entity matching in online social networks. In: International conference on social computing, vol 2013, pp 339–344. https://doi.org/10.1109/SocialCom.2013.53
Peled O, Fire M, Rokach L, Elovici Y (2016) Matching entities across online social networks. Neurocomputing 210(Supplement C):91–106. sI: Behavior. Analysis In SN. https://doi.org/10.1016/j.neucom.2016.03.089. http://www.sciencedirect.com/science/article/pii/S0925231216306014
Peng J, Choo K-KR, Ashman H (2016) User profiling in intrusion detection: a review. J Netw Comput Appl 72(Supplement C):14–27. https://doi.org/10.1016/j.jnca.2016.06.012. http://www.sciencedirect.com/science/article/pii/S1084804516301412
Real R, Vargas JM (1996) The probabilistic basis of Jaccard’s index of similarity. Syst Biol 45(3):380–385. https://doi.org/10.2307/2413572
Winkler WE (1990) String comparator metrics and enhanced decision rules in the Fellegi–Sunter model of record linkage. In: Proceedings of the section on survey research, pp 354–359
Zhang Y, Tang J, Yang Z, Pei J, Yu PS (2015) Cosnet: connecting heterogeneous social networks with local and global consistency. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’15, ACM, New York, NY, USA, pp 1485–1494. https://doi.org/10.1145/2783258.2783268
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Lee, J., Hussain, R., Rivera, V. et al. Second-level degree-based entity resolution in online social networks. Soc. Netw. Anal. Min. 8, 19 (2018). https://doi.org/10.1007/s13278-018-0499-9
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DOI: https://doi.org/10.1007/s13278-018-0499-9