Abstract
We present PGPI+ (Partial Graph Profile Inference+) an improved algorithm for user profiling in online social networks. PGPI+ infers user profiles under the constraint of a partial social graph using rich information about users (e.g. group memberships, views and likes) and handles nominal and numeric attributes. Experimental results with 20,000 user profiles from the Pokec social network shows that PGPI+ predicts user profiles with considerably more accuracy and by accessing a smaller part of the social graph than five state-of-the-art algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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., et al.: Inferring the location of twitter messages based on user relationships. Transactions in GIS 15(6), 735–751 (2011)
Dougnon, R.Y., Fournier-Viger, P., Nkambou, R.: Inferring user profiles in online social networks using a partial social graph. In: Barbosa, D., Milios, E. (eds.) Canadian AI 2015. LNCS (LNAI), vol. 9091, pp. 84–99. Springer, Heidelberg (2015)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Dougnon, R.Y., Fournier-Viger, P., Lin, J.CW., Nkambou, R. (2015). Accurate Online Social Network User Profiling. In: Hölldobler, S., , Peñaloza, R., Rudolph, S. (eds) KI 2015: Advances in Artificial Intelligence. KI 2015. Lecture Notes in Computer Science(), vol 9324. Springer, Cham. https://doi.org/10.1007/978-3-319-24489-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-24489-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24488-4
Online ISBN: 978-3-319-24489-1
eBook Packages: Computer ScienceComputer Science (R0)