Abstract
Most algorithms for user profile inference in online social networks assume that the full social graph is available for training. This assumption is convenient in a research setting. However, in real-life, the full social graph is generally unavailable or may be very costly to obtain or update. Thus, several of these algorithms may be inapplicable or provide poor accuracy. Moreover, current approaches often do not exploit all the rich information that is available in social networks. In this paper, we address these challenges by proposing an algorithm named PGPI (Partial Graph Profile Inference) to accurately infer user profiles under the constraint of a partial social graph and without training. It is to our knowledge, the first algorithm that let the user control the trade-off between the amount of information accessed from the social graph and the accuracy of predictions. Moreover, it is also designed to use rich information about users such as group memberships, views and likes. An experimental evaluation with 11,247 Facebook user profiles shows that PGPI predicts user profiles more accurately and by accessing a smaller part of the social graph than four state-of-the-art algorithms. Moreover, an interesting result is that profile attributes such as status (student/professor) and gender can be predicted with more than 90% accuracy using PGPI.
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References
Blenn, N., Doerr, C., Shadravan, N., Van Mieghem, P.: How much do your friends know about you?: reconstructing private information from the friendship graph. In: Proc. of the Fifth Workshop on Social Network Systems, pp. 1–6. ACM (2012)
Chaabane, A., Acs, G., Kaafar, M.A.: You are what you like! information leakage through users interests. In: Proc. of the 19th Annual Network and Distributed System Security Symposium. The Internet Society (2012)
Chaudhari, G., Avadhanula, V., Sarawagi, S.: A few good predictions: selective node labeling in a social network. In: Proc. of the 7th ACM International Conference on Web Search and Data Mining, pp. 353–362. ACM (2014)
Davis Jr., C.A., Pappa, G.L., de Oliveira, D.R.R., de L Arcanjo, F.: Inferring the location of twitter messages based on user relationships. Transactions in GIS 15(6), 735–751 (2011)
Dong, Y., Yang, Y., Tang, J., Yang, Y., Chawla, V. N.: Inferring user demographics and social strategies in mobile social networks. In: Proc. of the 20th ACM International Conference on Knowledge Discovery and Data Mining, pp. 15–24. ACM (2014)
He, J., Chu, W.W., Liu, Z.V.: Inferring privacy information from social networks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, F.-Y. (eds.) ISI 2006. LNCS, vol. 3975, pp. 154–165. Springer, Heidelberg (2006)
Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Preventing private information inference attacks on social networks. IEEE Transactions on Knowledge and Data Engineering. 25(8), 1849–1862 (2013)
Jurgens, D.: Thats what friends are for: Inferring location in online social media platforms based on social relationships. In: Proc. of the 7th International AAAI Conference on Weblogs and Social Media, pp 273–282. AAAI Press (2013)
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. National Academy of Sciences 110(15), 5802–5805 (2013)
Li, R., Wang, C., Chang, K. C. C.: User profiling in an ego network: co-profiling attributes and relationships. In: Proc. of the 23rd International Conference on World Wide Web, pp. 819–830. ACM (2014)
Lindamood, J., Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Inferring private information using social network data. In: Proc. of the 18th International Conference on World Wide Web, pp. 1145–1146. ACM (2009)
Mislove, A., Viswanath, B., Gummadi, K. P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proc. of the 3rd ACM International Conference on Web Search and Data Mining, pp. 251–260. ACM (2010)
Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our Twitter profiles, our selves: Predicting personality with Twitter. In: Proc. of the 3rd IEEE International Conference on Social Computing, pp. 180–185. IEEE Press (2011)
Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of Facebook networks. Physica A: Statistical Mechanics and its Applications. 391(16), 4165–4180 (2012)
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Dougnon, R.Y., Fournier-Viger, P., Nkambou, R. (2015). Inferring User Profiles in Online Social Networks Using a Partial Social Graph. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_8
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DOI: https://doi.org/10.1007/978-3-319-18356-5_8
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